Learning Bayesian Statistics

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Use mini-batch methods to efficiently process large datasets within Bayesian frameworks in enterprise AI applications.
• Apply approximate inference techniques, like stochastic gradient MCMC and Laplace approximation, to optimize Bayesian analysis in practical settings.
• Explore thermodynamic computing to significantly speed up Bayesian computations, enhancing model efficiency and scalability.
• Leverage the Posteriors python package for flexible and integrated Bayesian analysis in modern machine learning workflows.
• Overcome challenges in Bayesian inference by simplifying complex concepts for non-expert audiences, ensuring the practical application of statistical models.
• Address the intricacies of model assumptions and communicate effectively to non-technical stakeholders to enhance decision-making processes.

Chapters:

00:00 Introduction to Large-Scale Machine Learning

11:26 Scalable and Flexible Bayesian Inference with Posteriors

25:56 The Role of Temperature in Bayesian Models

32:30 Stochastic Gradient MCMC for Large Datasets

36:12 Introducing Posteriors: Bayesian Inference in Machine Learning

41:22 Uncertainty Quantification and Improved Predictions

52:05 Supporting New Algorithms and Arbitrary Likelihoods

59:16 Thermodynamic Computing

01:06:22 Decoupling Model Specification, Data Generation, and Inference

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti.

Links from the show:

• Sam on Twitter: https://x.com/Sam_Duffield
• Sam on Scholar: https://scholar.google.com/citations?user=7wm_ka8AAAAJ&hl=en&oi=ao 
• Sam on Linkedin: https://www.linkedin.com/in/samduffield/
• Sam on GitHub: https://github.com/SamDuffield
• Posteriors paper (new!): https://arxiv.org/abs/2406.00104 
• Blog post introducing Posteriors: https://blog.normalcomputing.ai/posts/introducing-posteriors/posteriors.html
• Posteriors docs: https://normal-computing.github.io/posteriors/
• Paper introducing Posteriors – Scalable Bayesian Learning with posteriors: https://arxiv.org/abs/2406.00104v1
• Normal Computing scholar: https://scholar.google.com/citations?hl=en&user=jGCLWRUAAAAJ&view_op=list_works
• Thermo blogs: https://blog.normalcomputing.ai/posts/2023-11-09-thermodynamic-inversion/thermo-inversion.html
• https://blog.normalcomputing.ai/posts/thermox/thermox.html
• Great paper on SGMCMC: https://proceedings.neurips.cc/paper_files/paper/2015/file/9a4400501febb2a95e79248486a5f6d3-Paper.pdf
• David MacKay textbook on Sustainable Energy: https://www.withouthotair.com/
• LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
• LBS #98 - Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways

• Bayesian methods align better with researchers' intuitive understanding of research questions and provide more tools to evaluate and understand models.
• Prior sensitivity analysis is crucial for understanding the robustness of findings to changes in priors and helps in contextualizing research findings.
• Bayesian methods offer an elegant and efficient way to handle missing data in longitudinal studies, providing more flexibility and information for researchers.
• Fit indices in Bayesian model selection are effective in detecting underfitting but may struggle to detect overfitting, highlighting the need for caution in model complexity.
• Bayesian methods have the potential to revolutionize educational research by addressing the challenges of small samples, complex nesting structures, and longitudinal data. 
• Posterior predictive checks are valuable for model evaluation and selection.

Chapters

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers' Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti.

Links from the show

• Sonja’s website: https://winterstat.github.io/
• Sonja on Twitter: https://twitter.com/winterstat
• Sonja on GitHub: https://github.com/winterstat
• Under-Fitting and Over-Fitting – The Performance of Bayesian Model Selection and Fit Indices in SEM: https://www.tandfonline.com/doi/full/10.1080/10705511.2023.2280952
• LBS #102 – Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://youtu.be/lXd-qstzTh4?si=jLg_qZTt1oQqRO0R
• LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
• BayesFlow tutorial: https://bayesflow.org/_examples/Intro_Amortized_Posterior_Estimation.html
• LBS #106 Active Statistics, Two Truths & a Lie, with Andrew Gelman: https://learnbayesstats.com/episode/106-active-statistics-two-truths-a-lie-andrew-gelman/
• LBS #61 Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
• Bayesian Workflow paper: https://arxiv.org/abs/2011.01808
• Michael Betancourts'blog: https://betanalpha.github.io/writing/
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• Bayesian Model-Building Interface in Python: https://bambinos.github.io/bambi/
• Advanced Regression online course: https://www.intuitivebayes.com/advanced-regression
• BLIMP: https://www.appliedmissingdata.com/blimp 

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways

• Convincing non-stats stakeholders in sports analytics can be challenging, but building trust and confirming their prior beliefs can help in gaining acceptance.
• Combining subjective beliefs with objective data in Bayesian analysis leads to more accurate forecasts.
• The availability of massive data sets has revolutionized sports analytics, allowing for more complex and accurate models.
• Sports analytics models should consider factors like rest, travel, and altitude to capture the full picture of team performance.
• The impact of budget on team performance in American sports and the use of plus-minus models in basketball and American football are important considerations in sports analytics.
• The future of sports analytics lies in making analysis more accessible and digestible for everyday fans.
• There is a need for more focus on estimating distributions and variance around estimates in sports analytics.
• AI tools can empower analysts to do their own analysis and make better decisions, but it's important to ensure they understand the assumptions and structure of the data.
• Measuring the value of certain positions, such as midfielders in soccer, is a challenging problem in sports analytics.
• Game theory plays a significant role in sports strategies, and optimal strategies can change over time as the game evolves.

Chapters

00:00 Introduction and Overview

09:27 The Power of Bayesian Analysis in Sports Modeling

16:28 The Revolution of Massive Data Sets in Sports Analytics

31:03 The Impact of Budget in Sports Analytics

39:35 Introduction to Sports Analytics

52:22 Plus-Minus Models in American Football

01:04:11 The Future of Sports Analytics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti.

Links from the show:

• LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4
• Paul’s website: https://sabinanalytics.com/
• Paul on GitHub: https://github.com/sabinanalytics 
• Paul on Linkedin: https://www.linkedin.com/in/rpaulsabin/
• Paul on Twitter: https://twitter.com/SabinAnalytics
• Paul on Google Scholar: https://scholar.google.com/citations?user=wAezxZ4AAAAJ&hl=en
• Soccer Power Ratings & Projections: https://sabinanalytics.com/ratings/soccer/
• Estimating player value in American football using plus–minus models: https://www.degruyter.com/document/doi/10.1515/jqas-2020-0033/html
• World Football R Package: https://github.com/JaseZiv/worldfootballR

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

In this episode, Marvin Schmitt introduces the concept of amortized Bayesian inference, where the upfront training phase of a neural network is followed by fast posterior inference.

Marvin will guide us through this new concept, discussing his work in probabilistic machine learning and uncertainty quantification, using Bayesian inference with deep neural networks. 

He also introduces BayesFlow, a Python library for amortized Bayesian workflows, and discusses its use cases in various fields, while also touching on the concept of deep fusion and its relation to multimodal simulation-based inference.

A PhD student in computer science at the University of Stuttgart, Marvin is supervised by two LBS guests you surely know — Paul Bürkner and Aki Vehtari. Marvin’s research combines deep learning and statistics, to make Bayesian inference fast and trustworthy. 

In his free time, Marvin enjoys board games and is a passionate guitar player.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

• Amortized Bayesian inference combines deep learning and statistics to make posterior inference fast and trustworthy.
• Bayesian neural networks can be used for full Bayesian inference on neural network weights.
• Amortized Bayesian inference decouples the training phase and the posterior inference phase, making posterior sampling much faster.
• BayesFlow is a Python library for amortized Bayesian workflows, providing a user-friendly interface and modular architecture.
• Self-consistency loss is a technique that combines simulation-based inference and likelihood-based Bayesian inference, with a focus on amortization
• The BayesFlow package aims to make amortized Bayesian inference more accessible and provides sensible default values for neural networks.
• Deep fusion techniques allow for the fusion of multiple sources of information in neural networks.
• Generative models that are expressive and have one-step inference are an emerging topic in deep learning and probabilistic machine learning.
• Foundation models, which have a large training set and can handle out-of-distribution cases, are another intriguing area of research.

Chapters:

00:00 Introduction to Amortized Bayesian Inference

07:39 Bayesian Neural Networks

11:47 Amortized Bayesian Inference and Posterior Inference

23:20 BayesFlow: A Python Library for Amortized Bayesian Workflows

38:15 Self-consistency loss: Bridging Simulation-Based Inference and Likelihood-Based Bayesian Inference

41:35 Amortized Bayesian Inference

43:53 Fusing Multiple Sources of Information

45:19 Compensating for Missing Data

56:17 Emerging Topics: Expressive Generative Models and Foundation Models

01:06:18 The Future of Deep Learning and Probabilistic Machine Learning

Links from the show:

• Marvin’s website: https://www.marvinschmitt.com/
• Marvin on GitHub: https://github.com/marvinschmitt
• Marvin on Linkedin: https://www.linkedin.com/in/marvin-schmitt/
• Marvin on Twitter: https://twitter.com/MarvinSchmittML
• The BayesFlow package for amortized Bayesian workflows: https://bayesflow.org/ 
• BayesFlow Forums for users: https://discuss.bayesflow.org
• BayesFlow software paper (JOSS): https://joss.theoj.org/papers/10.21105/joss.05702
• Tutorial on amortized Bayesian inference with BayesFlow (Python): https://colab.research.google.com/drive/1ub9SivzBI5fMbSTwVM1pABsMlRupgqRb?usp=sharing 
• Towards Reliable Amortized Bayesian Inference: https://www.marvinschmitt.com/speaking/pdf/slides_reliable_abi_botb.pdf
• Expand the model space that we amortize over (multiverse analyses, power scaling, …): “Sensitivity-Aware Amortized Bayesian Inference” https://arxiv.org/abs/2310.11122
• Use heterogeneous data sources in amortized inference: “Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference” https://arxiv.org/abs/2311.10671
• Use likelihood density information (explicit or even learned on the fly): “Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference”  https://arxiv.org/abs/2310.04395
• LBS #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/
• LBS #101 Black Holes Collisions & Gravitational Waves, with LIGO Experts Christopher Berry & John Veitch: https://learnbayesstats.com/episode/101-black-holes-collisions-gravitational-waves-ligo-experts-christopher-berry-john-veitch/
• Deep Learning book: https://www.deeplearningbook.org/
• Statistical Rethinking: https://xcelab.net/rm/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

If there is one guest I don’t need to introduce, it’s mister Andrew Gelman. So… I won’t! I will refer you back to his two previous appearances on the show though, because learning from Andrew is always a pleasure. So go ahead and listen to episodes 20 and 27.

In this episode, Andrew and I discuss his new book, Active Statistics, which focuses on teaching and learning statistics through active student participation. Like this episode, the book is divided into three parts: 1) The ideas of statistics, regression, and causal inference; 2) The value of storytelling to make statistical concepts more relatable and interesting; 3) The importance of teaching statistics in an active learning environment, where students are engaged in problem-solving and discussion.

And Andrew is so active and knowledgeable that we of course touched on a variety of other topics — but for that, you’ll have to listen ;)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Active learning is essential for teaching and learning statistics.

- Storytelling can make statistical concepts more relatable and interesting.

- Teaching statistics in an active learning environment engages students in problem-solving and discussion.

- The book Active Statistics includes 52 stories, class participation activities, computer demonstrations, and homework assignments to facilitate active learning.

- Active learning, where students actively engage with the material through activities and discussions, is an effective approach to teaching statistics.

- The flipped classroom model, where students read and prepare before class and engage in problem-solving activities during class, can enhance learning and understanding.

- Clear organization and fluency in teaching statistics are important for student comprehension and engagement.

- Visualization plays a crucial role in understanding statistical concepts and aids in comprehension.

- The future of statistical education may involve new approaches and technologies, but the challenge lies in finding effective ways to teach basic concepts and make them relevant to real-world problems.

Chapters:

00:00 Introduction and Background

08:09 The Importance of Stories in Statistics Education

30:28 Using 'Two Truths and a Lie' to Teach Logistic Regression

38:08 The Power of Storytelling in Teaching Statistics

57:26 The Importance of Visualization in Understanding Statistics

01:07:03 The Future of Statistical Education

Links from the show:

• Andrew’s website: http://www.stat.columbia.edu/~gelman/
• Andrew’s blog: https://statmodeling.stat.columbia.edu/ 
• Twitter links to blog posts: https://twitter.com/statmodeling
• Active Statistics page: http://www.stat.columbia.edu/~gelman/active-statistics/
• “Two truths and a lie” as a class-participation activity: http://www.stat.columbia.edu/~gelman/research/published/truths_paper.pdf
• Rohan Alexander’s book, Telling Stories with Data:  https://tellingstorieswithdata.com/
• Use code ACTSTAT24 to buy Active Statistics with 20% off through July 15, 2024: www.cambridge.org/9781009436212
• LBS #27, Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns: https://learnbayesstats.com/episode/27-modeling-the-us-presidential-elections-with-andrew-gelman-merlin-heidemanns/
• LBS #20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/
• Slamming the sham – A Bayesian model for adaptive adjustment with noisy control data: http://www.stat.columbia.edu/~gelman/research/unpublished/chickens.pdf

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

In this episode, Andy Aschwanden and Doug Brinkerhoff tell us about their work in glaciology and the application of Bayesian statistics in studying glaciers. They discuss the use of computer models and data analysis in understanding glacier behavior and predicting sea level rise, and a lot of other fascinating topics.

Andy grew up in the Swiss Alps, and studied Earth Sciences, with a focus on atmospheric and climate science and glaciology. After his PhD, Andy moved to Fairbanks, Alaska, and became involved with the Parallel Ice Sheet Model, the first open-source and openly-developed ice sheet model.

His first PhD student was no other than… Doug Brinkerhoff! Doug did an MS in computer science at the University of Montana, focusing on numerical methods for ice sheet modeling, and then moved to Fairbanks to complete his PhD. While in Fairbanks, he became an ardent Bayesian after “seeing that uncertainty needs to be embraced rather than ignored”. Doug has since moved back to Montana, becoming faculty in the University of Montana’s computer science department.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero and Will Geary.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Computer models and data analysis play a crucial role in understanding glacier behavior and predicting sea level rise.

- Reliable data, especially on ice thickness and climate forcing, are essential for accurate modeling.

- The collaboration between glaciology and Bayesian statistics has led to breakthroughs in understanding glacier evolution forecasts.

-There is a need for open-source packages and tools to make glaciological models more accessible. Glaciology and ice sheet modeling are complex fields that require collaboration between domain experts and data scientists.

- The use of Bayesian statistics in glaciology allows for a probabilistic framework to understand and communicate uncertainty in predictions.

- Real-time forecasting of glacier behavior is an exciting area of research that could provide valuable information for communities living near glaciers.

-There is a need for further research in understanding existing data sets and developing simpler methods to analyze them.

- The future of glaciology research lies in studying Alaskan glaciers and understanding the challenges posed by the changing Arctic environment.

Chapters:

00:00 Introduction and Background

08:54 The Role of Statistics in Glaciology

31:46 Open-Source Packages and Tools

52:06 The Power of Bayesian Statistics in Glaciology

01:06:34 Understanding Existing Data Sets and Developing Simpler Methods

Links from the show:

• Andy’s website: https://glaciers.gi.alaska.edu/people/aschwanden
• Doug’s website: https://dbrinkerhoff.org/
• Andy on GitHub: https://github.com/aaschwanden 
• Doug on GitHub: https://github.com/douglas-brinkerhoff/
• Andy on Twitter: https://twitter.com/glacierandy?lang=fr
• Andy on Google Scholar: https://scholar.google.com/citations?user=CuvsLvMAAAAJ&hl=en
• Doug on Google Scholar: https://scholar.google.com/citations?user=FqU6ON8AAAAJ&hl=en
• LBS #64, Modeling the Climate & Gravity Waves, with Laura Mansfield: https://learnbayesstats.com/episode/64-modeling-climate-gravity-waves-laura-mansfield/
• Parallel Ice Sheet Model: www.pism.io
• PISM on GitHub: https://github.com/pism/pism
• Greenland View of Three Simulated Greenland Ice Sheet Response Scenarios: https://svs.gsfc.nasa.gov/4727/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

GPs are extremely powerful…. but hard to handle. One of the bottlenecks is learning the appropriate kernel. What if you could learn the structure of GP kernels automatically? Sounds really cool, but also a bit futuristic, doesn’t it?

Well, think again, because in this episode, Feras Saad will teach us how to do just that! Feras is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD in Computer Science from MIT, and, most importantly for our conversation, he’s the creator of AutoGP.jl, a Julia package for automatic Gaussian process modeling.

Feras discusses the implementation of AutoGP, how it scales, what you can do with it, and how you can integrate its outputs in your models.

Finally, Feras provides an overview of Sequential Monte Carlo and its usefulness in AutoGP, highlighting the ability of SMC to incorporate new data in a streaming fashion and explore multiple modes efficiently.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell and Gal Kampel.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- AutoGP is a Julia package for automatic Gaussian process modeling that learns the structure of GP kernels automatically.

- It addresses the challenge of making structural choices for covariance functions by using a symbolic language and a recursive grammar to infer the expression of the covariance function given the observed data.

-AutoGP incorporates sequential Monte Carlo inference to handle scalability and uncertainty in structure learning.

- The package is implemented in Julia using the Gen probabilistic programming language, which provides support for sequential Monte Carlo and involutive MCMC.

- Sequential Monte Carlo (SMC) and inductive MCMC are used in AutoGP to infer the structure of the model.

- Integrating probabilistic models with language models can improve interpretability and trustworthiness in data-driven inferences.

- Challenges in Bayesian workflows include the need for automated model discovery and scalability of inference algorithms.

- Future developments in probabilistic reasoning systems include unifying people around data-driven inferences and improving the scalability and configurability of inference algorithms.

Chapters:

00:00 Introduction to AutoGP

26:28 Automatic Gaussian Process Modeling

45:05 AutoGP: Automatic Discovery of Gaussian Process Model Structure

53:39 Applying AutoGP to New Settings

01:09:27 The Biggest Hurdle in the Bayesian Workflow

01:19:14 Unifying People Around Data-Driven Inferences

Links from the show:

• Sign up to the Fast & Efficient Gaussian Processes modeling webinar: https://topmate.io/alex_andorra/901986
• Feras’ website: https://www.cs.cmu.edu/~fsaad/
• LBS #3.1, What is Probabilistic Programming & Why use it, with Colin Carroll: https://learnbayesstats.com/episode/3-1-what-is-probabilistic-programming-why-use-it-with-colin-carroll/
• LBS #3.2, How to use Bayes in industry, with Colin Carroll: https://learnbayesstats.com/episode/3-2-how-to-use-bayes-in-industry-with-colin-carroll/
• LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/
• LBS #29, Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari: https://learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtari/
• LBS #63, Media Mix Models & Bayes for Marketing, with Luciano Paz: https://learnbayesstats.com/episode/63-media-mix-models-bayes-marketing-luciano-paz/
• LBS #83, Multilevel Regression, Post-Stratification & Electoral Dynamics, with Tarmo Jüristo: https://learnbayesstats.com/episode/83-multilevel-regression-post-stratification-electoral-dynamics-tarmo-juristo/
• AutoGP.jl, A Julia package for learning the covariance structure of Gaussian process time series models: https://probsys.github.io/AutoGP.jl/stable/
• Sequential Monte Carlo Learning for Time Series Structure Discovery: https://arxiv.org/abs/2307.09607
• Street Epistemlogy: https://www.youtube.com/@magnabosco210
• You're not so smart Podcast: https://youarenotsosmart.com/podcast/
• How Minds Change: https://www.davidmcraney.com/howmindschangehome
• Josh Tenebaum's lectures on computational cognitive science: https://www.youtube.com/playlist?list=PLUl4u3cNGP61RTZrT3MIAikp2G5EEvTjf

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Changing perspective is often a great way to solve burning research problems. Riemannian spaces are such a perspective change, as Arto Klami, an Associate Professor of computer science at the University of Helsinki and member of the Finnish Center for Artificial Intelligence, will tell us in this episode.

He explains the concept of Riemannian spaces, their application in inference algorithms, how they can help sampling Bayesian models, and their similarity with normalizing flows, that we discussed in episode 98.

Arto also introduces PreliZ, a tool for prior elicitation, and highlights its benefits in simplifying the process of setting priors, thus improving the accuracy of our models.

When Arto is not solving mathematical equations, you’ll find him cycling, or around a good board game.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Riemannian spaces offer a way to improve computational efficiency and accuracy in Bayesian inference by considering the curvature of the posterior distribution.

- Riemannian spaces can be used in Laplace approximation and Markov chain Monte Carlo algorithms to better model the posterior distribution and explore challenging areas of the parameter space.

- Normalizing flows are a complementary approach to Riemannian spaces, using non-linear transformations to warp the parameter space and improve sampling efficiency.

- Evaluating the performance of Bayesian inference algorithms in challenging cases is a current research challenge, and more work is needed to establish benchmarks and compare different methods. 

- PreliZ is a package for prior elicitation in Bayesian modeling that facilitates communication with users through visualizations of predictive and parameter distributions.

- Careful prior specification is important, and tools like PreliZ make the process easier and more reproducible.

- Teaching Bayesian machine learning is challenging due to the combination of statistical and programming concepts, but it is possible to teach the basic reasoning behind Bayesian methods to a diverse group of students.

- The integration of Bayesian approaches in data science workflows is becoming more accepted, especially in industries that already use deep learning techniques.

- The future of Bayesian methods in AI research may involve the development of AI assistants for Bayesian modeling and probabilistic reasoning.

Chapters:

00:00 Introduction and Background

02:05 Arto's Work and Background

06:05 Introduction to Bayesian Inference

12:46 Riemannian Spaces in Bayesian Inference

27:24 Availability of Romanian-based Algorithms

30:20 Practical Applications and Evaluation

37:33 Introduction to Prelease

38:03 Prior Elicitation

39:01 Predictive Elicitation Techniques

39:30 PreliZ: Interface with Users

40:27 PreliZ: General Purpose Tool

41:55 Getting Started with PreliZ

42:45 Challenges of Setting Priors

45:10 Reproducibility and Transparency in Priors

46:07 Integration of Bayesian Approaches in Data Science Workflows

55:11 Teaching Bayesian Machine Learning

01:06:13 The Future of Bayesian Methods with AI Research

01:10:16 Solving the Prior Elicitation Problem

Links from the show:

• LBS #29, Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari: https://learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtari/
• LBS #20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/
• LBS #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/
• Arto’s website: https://www.cs.helsinki.fi/u/aklami/
• Arto on Google Scholar: https://scholar.google.com/citations?hl=en&user=v8PeLGgAAAAJ
• Multi-source probabilistic inference Group: https://www.helsinki.fi/en/researchgroups/multi-source-probabilistic-inference
• FCAI web page: https://fcai.fi
• Probabilistic AI summer school lectures: https://www.youtube.com/channel/UCcMwNzhpePJE3xzOP_3pqsw
• Keynote: "Better priors for everyone" by Arto Klami: https://www.youtube.com/watch?v=mEmiEHsfWyc&ab_channel=ProbabilisticAISchool
• Variational Inference and Optimization I by Arto Klami: https://www.youtube.com/watch?v=60USDNc1nE8&list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik&index=3&ab_channel=ProbabilisticAISchool
• PreliZ, A tool-box for prior elicitation: https://preliz.readthedocs.io/en/latest/
• AISTATS paper that presents the new computationally efficient metric in context of MCMC: https://researchportal.helsinki.fi/en/publications/lagrangian-manifold-monte-carlo-on-monge-patches
• TMLR paper that scales up the solution for larger models, using the metric for sampling-based inference in deel learning: https://openreview.net/pdf?id=dXAuvo6CGI
• Riemannian Laplace approximation (to appear in AISTATS’24): https://arxiv.org/abs/2311.02766
• Prior Knowledge Elicitation -- The Past, Present, and Future: https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Prior-Knowledge-Elicitation-The-Past-Present-and-Future/10.1214/23-BA1381.full

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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Structural Equation Modeling (SEM) is a key framework in causal inference. As I’m diving deeper and deeper into these topics to teach them and, well, finally understand them, I was delighted to host Ed Merkle on the show.

A professor of psychological sciences at the University of Missouri, Ed discusses his work on Bayesian applications to psychometric models and model estimation, particularly in the context of Bayesian SEM. He explains the importance of BSEM in psychometrics and the challenges encountered in its estimation.

Ed also introduces his blavaan package in R, which enhances researchers' capabilities in BSEM and has been instrumental in the dissemination of these methods. Additionally, he explores the role of Bayesian methods in forecasting and crowdsourcing wisdom.

When he’s not thinking about stats and psychology, Ed can be found running, playing the piano, or playing 8-bit video games.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Bayesian SEM is a powerful framework in psychometrics that allows for the estimation of complex models involving multiple variables and causal relationships.

- Understanding the principles of Bayesian inference is crucial for effectively applying Bayesian SEM in psychological research.

- Informative priors play a key role in Bayesian modeling, providing valuable information and improving the accuracy of model estimates.

- Challenges in BSEM estimation include specifying appropriate prior distributions, dealing with unidentified parameters, and ensuring convergence of the model. Incorporating prior information is crucial in Bayesian modeling, especially when dealing with large models and imperfect data.

- The blavaan package enhances researchers' capabilities in Bayesian structural equation modeling, providing a user-friendly interface and compatibility with existing frequentist models.

- Bayesian methods offer advantages in forecasting and subjective probability by allowing for the characterization of uncertainty and providing a range of predictions.

- Interpreting Bayesian model results requires careful consideration of the entire posterior distribution, rather than focusing solely on point estimates.

- Latent variable models, also known as structural equation models, play a crucial role in psychometrics, allowing for the estimation of unobserved variables and their influence on observed variables.

- The speed of MCMC estimation and the need for a slower, more thoughtful workflow are common challenges in the Bayesian workflow.

- The future of Bayesian psychometrics may involve advancements in parallel computing and GPU-accelerated MCMC algorithms.

Chapters:

00:00 Introduction to the Conversation

02:17 Background and Work on Bayesian SEM

04:12 Topics of Focus: Structural Equation Models

05:16 Introduction to Bayesian Inference

09:30 Importance of Bayesian SEM in Psychometrics

10:28 Overview of Bayesian Structural Equation Modeling (BSEM)

12:22 Relationship between BSEM and Causal Inference

15:41 Advice for Learning BSEM

21:57 Challenges in BSEM Estimation

34:40 The Impact of Model Size and Data Quality

37:07 The Development of the Blavaan Package

42:16 Bayesian Methods in Forecasting and Subjective Probability

46:27 Interpreting Bayesian Model Results

51:13 Latent Variable Models in Psychometrics

56:23 Challenges in the Bayesian Workflow

01:01:13 The Future of Bayesian Psychometrics

Links from the show:

• Ed’s website: https://ecmerkle.github.io/
• Ed on Mastodon: https://mastodon.sdf.org/@edgarmerkle
• Ed on BlueSky: @edgarmerkle.bsky.social
• Ed on GitHub: https://github.com/ecmerkle
• blaavan R package: https://ecmerkle.github.io/blavaan/
• Resources on how to use blaavan: https://ecmerkle.github.io/blavaan/articles/resources.html
• Richard McElreath, Table 2 Fallacy: https://youtu.be/uanZZLlzKHw?si=vssrwJsvGO5HhH5H&t=4323

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/101-black-holes-collisions-gravitational-waves-ligo-experts-christopher-berry-john-veitch/

Watch the interview: https://www.youtube.com/watch?v=ZaZwCcrJlik

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/101-black-holes-collisions-gravitational-waves-ligo-experts-christopher-berry-john-veitch/

Watch the interview: https://www.youtube.com/watch?v=ZaZwCcrJlik

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

In this episode, we dive deep into gravitational wave astronomy, with Christopher Berry and John Veitch, two senior lecturers at the University of Glasgow and experts from the LIGO-VIRGO collaboration. They explain the significance of detecting gravitational waves, which are essential for understanding black holes and neutron stars collisions. This research not only sheds light on these distant events but also helps us grasp the fundamental workings of the universe.

Our discussion focuses on the integral role of Bayesian statistics, detailing how they use nested sampling for extracting crucial information from the subtle signals of gravitational waves. This approach is vital for parameter estimation and understanding the distribution of cosmic sources through population inferences.

Concluding the episode, Christopher and John highlight the latest advancements in black hole astrophysics and tests of general relativity, and touch upon the exciting prospects and challenges of the upcoming space-based LISA mission.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

 ⁃    Gravitational wave analysis involves using Bayesian statistics for parameter estimation and population inference.

    ⁃    Nested sampling is a powerful algorithm used in gravitational wave analysis to explore parameter space and calculate the evidence for model selection.

    ⁃    Machine learning techniques, such as normalizing flows, can be integrated with nested sampling to improve efficiency and explore complex distributions.

    ⁃    The LIGO-VIRGO collaboration operates gravitational wave detectors that measure distortions in space and time caused by black hole and neutron star collisions.

    ⁃    Sources of noise in gravitational wave detection include laser noise, thermal noise, seismic motion, and gravitational coupling.

    ⁃    The LISA mission is a space-based gravitational wave detector that aims to observe lower frequency gravitational waves and unlock new astrophysical phenomena.

    ⁃    Space-based detectors like LISA can avoid the ground-based noise and observe a different part of the gravitational wave spectrum, providing new insights into the universe.

    ⁃    The data analysis challenges for space-based detectors are complex, as they require fitting multiple sources simultaneously and dealing with overlapping signals.

    ⁃    Gravitational wave observations have the potential to test general relativity, study the astrophysics of black holes and neutron stars, and provide insights into cosmology.

Links from the show:

• Christopher’s’ website: https://cplberry.com/
• John’s website: https://www.veitch.me.uk/
• Christopher on GitHub: https://github.com/cplb/ 
• John on GitHub: https://github.com/johnveitch
• Christopher on Linkedin: http://www.linkedin.com/in/cplberry 
• John on Linkedin: https://www.linkedin.com/in/john-veitch-56772225/
• Christopher on Twitter: https://twitter.com/cplberry
• John on Twitter: https://twitter.com/johnveitch
• Christopher on Mastodon: https://mastodon.scot/@[email protected] 
• John on Mastodon: https://mastodon.scot/@JohnVeitch
• LIGO website: https://www.ligo.org/
• LIGO Gitlab: https://git.ligo.org/users/sign_in
• Gravitational Wave Open Science Center: https://gwosc.org/
• LIGO Caltech Lab: https://www.ligo.caltech.edu/page/ligo-data
• Exoplanet, python package for probabilistic modeling of time series data in astronomy: https://docs.exoplanet.codes/en/latest/
• Dynamic Nested Sampling with dynesty: https://dynesty.readthedocs.io/en/latest/dynamic.html
• Nessai, Nested sampling with artificial intelligence: https://nessai.readthedocs.io/
• LBS #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/
• bayeux, JAX models with state-of-the-art inference methods: https://jax-ml.github.io/bayeux/
• LBS #51 Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• Aubrey Clayton's Probability Theory Lectures based on E.T Jaynes book: https://www.youtube.com/playlist?list=PL9v9IXDsJkktefQzX39wC2YG07vw7DsQ_

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/100-reactive-message-passing-automated-inference-in-julia-dmitry-bagaev/

Watch the interview: https://www.youtube.com/watch?v=ZG3H0xxCXTQ

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/100-reactive-message-passing-automated-inference-in-julia-dmitry-bagaev/

Watch the interview: https://www.youtube.com/watch?v=ZG3H0xxCXTQ

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

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In this episode, Dmitry Bagaev discusses his work in Bayesian statistics and the development of RxInfer.jl, a reactive message passing toolbox for Bayesian inference. 

Dmitry explains the concept of reactive message passing and its applications in real-time signal processing and autonomous systems. He discusses the challenges and benefits of using RxInfer.jl, including its scalability and efficiency in large probabilistic models. 

Dmitry also shares insights into the trade-offs involved in Bayesian inference architecture and the role of variational inference in RxInfer.jl. Additionally, he discusses his startup Lazy Dynamics and its goal of commercializing research in Bayesian inference. 

Finally, we also discuss the user-friendliness and trade-offs of different inference methods, the future developments of RxInfer, and the future of automated Bayesian inference. 

Coming from a very small town in Russia called Nizhnekamsk, Dmitry currently lives in the Netherlands, where he did his PhD. Before that, he graduated from the Computational Science and Modeling department of Moscow State University. 

Beyond that, Dmitry is also a drummer (you’ll see his cool drums if you’re watching on YouTube), and an adept of extreme sports, like skydiving, wakeboarding and skiing!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Reactive message passing is a powerful approach to Bayesian inference that allows for real-time updates and adaptivity in probabilistic models.

- RxInfer.jl is a toolbox for reactive message passing in Bayesian inference, designed to be scalable, efficient, and adaptable.

- Julia is a preferred language for RxInfer.jl due to its speed, macros, and multiple dispatch, which enable efficient and flexible implementation.

- Variational inference plays a crucial role in RxInfer.jl, allowing for trade-offs between computational complexity and accuracy in Bayesian inference.

- Lazy Dynamics is a startup focused on commercializing research in Bayesian inference, with the goal of making RxInfer.jl accessible and robust for industry applications.

Links from the show:

• LBS Physics & Astrophysics playlist: https://learnbayesstats.com/physics-astrophysics/
• LBS #51, Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• Dmitry on GitHub: https://github.com/bvdmitri
• Dmitry on LinkedIn: https://www.linkedin.com/in/bvdmitri/
• RxInfer.jl, Automatic Bayesian Inference through Reactive Message Passing: https://rxinfer.ml/
• Reactive Bayes, Open source software for reactive, efficient and scalable Bayesian inference: https://github.com/ReactiveBayes
• LazyDynamics, Reactive Bayesian AI: https://lazydynamics.com/
• BIASlab, Natural Artificial Intelligence: https://biaslab.github.io/
• Dmitry's PhD dissertation: https://research.tue.nl/en/publications/reactive-probabilistic-programming-for-scalable-bayesian-inferenc
• Effortless Mastery, by Kenny Werner: https://www.amazon.com/Effortless-Mastery-Liberating-Master-Musician/dp/156224003X
• The Book of Why, by Judea Pearl: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X
• Bernoulli’s Fallacy, by Aubrey Clayton: https://www.amazon.com/Bernoullis-Fallacy-Statistical-Illogic-Science/dp/0231199945
• Software Engineering for Science: https://www.amazon.com/Software-Engineering-Science-Chapman-Computational/dp/1498743854

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/99-exploring-quantum-physics-bayesian-stats-chris-ferrie/

Watch the interview: https://www.youtube.com/watch?v=pRaT6FLF7A8

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/99-exploring-quantum-physics-bayesian-stats-chris-ferrie/

Watch the interview: https://www.youtube.com/watch?v=pRaT6FLF7A8

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
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You know I’m a big fan of everything physics. So when I heard that Bayesian stats was especially useful in quantum physics, I had to make an episode about it!

You’ll hear from Chris Ferrie, an Associate Professor at the Centre for Quantum Software and Information of the University of Technology Sydney. Chris also has a foot in industry, as a co-founder of Eigensystems, an Australian start-up with a mission to democratize access to quantum computing. 

Of course, we talked about why Bayesian stats are helpful in quantum physics research, and about the burning challenges in this line of research.

But Chris is also a renowned author — in addition to writing Bayesian Probability for Babies, he is the author of Quantum Physics for Babies and Quantum Bullsh*t: How to Ruin Your Life With Advice from Quantum Physics. So we ended up talking about science communication, science education, and a shocking revelation about Ant Man…

A big thank you to one of my best Patrons, Stefan Lorenz, for recommending me an episode with Chris!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

• Quantum computing has the potential to revolutionize various industries, but it requires specialized tools and education to fully harness its power.
• Bayesian inference plays a crucial role in understanding and solving problems in quantum physics, particularly in parameter estimation and model building.
• The field of quantum physics faces challenges in experimental design, data collection, and maintaining the state of isolated quantum systems.
• There is a need for specialized software that can accommodate the unique constraints and models in quantum physics, allowing for more efficient and accurate analysis.
• Common misconceptions in quantum physics include the idea of superposition as being in two places at once and the misinterpretation of quantum experiments. Misconceptions in quantum physics and Bayesian probability are common and can be addressed through clear explanations and analogies.
• Communicating scientific concepts to the general public requires bridging the gap between scientific papers and mainstream media.
• Simplifying complex topics for young minds involves providing relatable examples, analogies, and categories.
• Studying mathematics is essential for a deeper understanding of quantum physics and statistics.
• Taking risks and making mistakes is encouraged in the early stages of a scientific career.

Links from the show:

• Chris’ website: https://www.csferrie.com/
• Chris on Linkedin: https://www.linkedin.com/in/christopher-ferrie-63993190/
• Chris on Twitter: https://twitter.com/csferrie
• Chris on Instagram: https://www.instagram.com/drchrisferrie/
• Chris’ YouTube channel: https://www.youtube.com/csferrie
• Chris’ children’s books: https://www.amazon.com/gp/product/149267043X
• How quantum mechanics turned me into a Bayesian: https://csferrie.medium.com/how-quantum-mechanics-turned-me-into-a-bayesian-655ddf88051f
• Exoplanet, a python package for probabilistic modeling of time series data in astronomy: https://docs.exoplanet.codes/en/latest/
• Quantum Bullsh*t – How to Ruin Your Life with Advice from Quantum Physics : https://www.goodreads.com/en/book/show/61263731
• LBS #93, A CERN Odyssey, with Kevin Greif: https://www.youtube.com/watch?v=rOaqIIEtdpI
• LBS #72, Why the Universe is so Deliciously Crazy, with Daniel Whiteson: https://learnbayesstats.com/episode/72-why-the-universe-is-so-deliciously-crazy-daniel-whiteson/
• LBS #97, Probably Overthinking Statistical Paradoxes, with Allen Downey: https://learnbayesstats.com/episode/97-probably-overthinking-statistical-paradoxes-allen-downey/
• LBS #51, Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/

Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/

Watch the interview: https://www.youtube.com/watch?v=vVqZ0WWXX7g

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

How does the world of statistical physics intertwine with machine learning, and what groundbreaking insights can this fusion bring to the field of artificial intelligence?

In this episode, we delve into these intriguing questions with Marylou Gabrié. an assistant professor at CMAP, Ecole Polytechnique in Paris. Having completed her PhD in physics at École Normale Supérieure, Marylou ventured to New York City for a joint postdoctoral appointment at New York University’s Center for Data Science and the Flatiron’s Center for Computational Mathematics.

As you’ll hear, her research is not just about theoretical exploration; it also extends to the practical adaptation of machine learning techniques in scientific contexts, particularly where data is scarce.

In this conversation, we’ll traverse the landscape of Marylou's research, discussing her recent publications and her innovative approaches to machine learning challenges, latest MCMC advances, and ML-assisted scientific computing.

Beyond that, get ready to discover the person behind the science – her inspirations, aspirations, and maybe even what she does when not decoding the complexities of machine learning algorithms!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways

• Developing methods that leverage machine learning for scientific computing can provide valuable insights into high-dimensional probabilistic models.
• Generative models can be used to speed up Markov Chain Monte Carlo (MCMC) methods and improve the efficiency of sampling from complex distributions.
• The Adaptive Monte Carlo algorithm augmented with normalizing flows offers a powerful approach for sampling from multimodal distributions.
• Scaling the algorithm to higher dimensions and handling discrete parameters are ongoing challenges in the field.
• Open-source packages, such as Flow MC, provide valuable tools for researchers and practitioners to adopt and contribute to the development of new algorithms. The scaling of algorithms depends on the quantity of parameters and data. While some methods work well with a few hundred parameters, larger quantities can lead to difficulties.
• Generative models, such as normalizing flows, offer benefits in the Bayesian context, including amortization and the ability to adjust the model with new data.
• Machine learning and MCMC are complementary and should be used together rather than replacing one another.
• Machine learning can assist scientific computing in the context of scarce data, where expensive experiments or numerics are required.
• The future of MCMC lies in the exploration of sampling multimodal distributions and understanding resource limitations in scientific research.

Links from the show:

• Marylou’s website: https://marylou-gabrie.github.io/
• Marylou on Linkedin: https://www.linkedin.com/in/marylou-gabri%C3%A9-95366172/
• Marylou on Twitter: https://twitter.com/marylougab
• Marylou on Github: https://github.com/marylou-gabrie
• Marylou on Google Scholar: https://scholar.google.fr/citations?hl=fr&user=5m1DvLwAAAAJ
• Adaptive Monte Carlo augmented with normalizing flows: https://arxiv.org/abs/2105.12603
• Normalizing-flow enhanced sampling package for probabilistic inference: https://flowmc.readthedocs.io/en/main/
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.100.034515
• Boltzmann generators – Sampling equilibrium states of many-body systems with deep learning: https://www.science.org/doi/10.1126/science.aaw1147
• Solving Statistical Mechanics Using Variational Autoregressive Networks: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.080602
• An example of discrete version of similar algorithms:  https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.L042024
• Grothendieck's conference: https://www.youtube.com/watch?v=ZW9JpZXwGXc

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/97-probably-overthinking-statistical-paradoxes-allen-downey/

Watch the interview: https://www.youtube.com/watch?v=KgesIe3hTe0

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/97-probably-overthinking-statistical-paradoxes-allen-downey/

Watch the interview: https://www.youtube.com/watch?v=KgesIe3hTe0

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
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In this episode, I had the pleasure of speaking with Allen Downey, a professor emeritus at Olin College and a curriculum designer at Brilliant.org. Allen is a renowned author in the fields of programming and data science, with books such as "Think Python" and "Think Bayes" to his credit. He also authors the blog "Probably Overthinking It" and has a new book by the same name, which he just released in December 2023.

In this conversation, we tried to help you differentiate between right and wrong ways of looking at statistical data, discussed the Overton paradox and the role of Bayesian thinking in it, and detailed a mysterious Bayesian killer app!

But that’s not all: we even addressed the claim that Bayesian and frequentist methods often yield the same results — and why it’s a false claim. If that doesn’t get you to listen, I don’t know what will!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie and Cory Kiser.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• LBS #41, Thinking Bayes, with Allen Downey: https://learnbayesstats.com/episode/41-think-bayes-allen-downey/
• Allen’s blog: https://www.allendowney.com/blog/
• Allen on Twitter: https://twitter.com/allendowney
• Allen on GitHub: https://github.com/AllenDowney
• Order Allen’s book, Probably Overthinking It, at a 30% discount with the code UCPNEW: https://press.uchicago.edu/ucp/books/book/chicago/P/bo206532752.html
• The Bayesian Killer App: https://www.allendowney.com/blog/2023/03/20/the-bayesian-killer-app/
• Bayesian and Frequentist Results Are Not the Same, Ever: https://www.allendowney.com/blog/2021/04/25/bayesian-and-frequentist-results-are-not-the-same-ever/
• Allen’s presentation on the Overton paradox: https://docs.google.com/presentation/d/1-Uvby1Lfe1BTsxNv5R6PhXfwkLUgsyJgdkKtO8nUfJo/edit#slide=id.g291c5d4559e_0_0
• Video on the Overton Paradox, from PyData NYC 2022: https://youtu.be/VpuWECpTxmM
• Thompson sampling as a dice game: https://allendowney.github.io/TheShakes/
• Causal quartets – Different ways to attain the same average treatment effect: http://www.stat.columbia.edu/~gelman/research/unpublished/causal_quartets.pdf
• LBS #89, Unlocking the Science of Exercise, Nutrition & Weight Management, with Eric Trexler: https://learnbayesstats.com/episode/89-unlocking-science-exercise-nutrition-weight-management-eric-trexler/
• How Minds Change, David McRaney: https://www.davidmcraney.com/howmindschangehome

Abstract

by Christoph Bamberg

We are happy to welcome Allen Downey back to ur show and he has great news for us: His new book “Probably Overthinking It” is available now. 

You might know Allen from his blog by the same name or his previous work. Or maybe you watched some of his educational videos which he produces in his new position at brilliant.org.

We delve right into exciting topics like collider bias and how it can explain the “low brith weight paradox” and other situations that only seem paradoxical at first, until you apply causal thinking to it.

Another classic Allen can unmystify for us is Simpson’s paradox. The problem is not the data, but your expectations of the data. We talk about some cases of Simpson’s paradox, for example from statistics on the Covid-19 pandemic, also featured in his book.

We also cover the “Overton paradox” - which Allen named himself - on how people report their ideologies as liberal or conservative over time. 

Next to casual thinking and statistical paradoxes, we return to the common claim that frequentist statistics and Bayesian statistics often give the same results. Allen explains that they are fundamentally different and that Bayesian should not shy away from pointing that out and to emphasise the strengths of their methods.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/96-pharma-models-sports-analytics-stan-news-daniel-lee/

Watch the interview: https://www.youtube.com/watch?v=lnq5ZPlup0E

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas and Luke Gorrie.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Listen to the full episode: https://learnbayesstats.com/episode/96-pharma-models-sports-analytics-stan-news-daniel-lee/

Watch the interview: https://www.youtube.com/watch?v=lnq5ZPlup0E

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas and Luke Gorrie.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
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Getting Daniel Lee on the show is a real treat — with 20 years of experience in numeric computation; 10 years creating and working with Stan; 5 years working on pharma-related models, you can ask him virtually anything. And that I did…

From joint models for estimating oncology treatment efficacy to PK/PD models; from data fusion for U.S. Navy applications to baseball and football analytics, as well as common misconceptions or challenges in the Bayesian world — our conversation spans a wide range of topics that I’m sure you’ll appreciate!

Daniel studied Mathematics at MIT and Statistics at Cambridge University, and, when he’s not in front of his computer, is a savvy basketball player and… a hip hop DJ — you actually have his SoundCloud profile in the show notes if you’re curious!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas and Luke Gorrie.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Daniel on Linkedin: https://www.linkedin.com/in/syclik/
• Daniel on Twitter: https://twitter.com/djsyclik
• Daniel on GitHub: https://github.com/syclik
• Daniel's DJ profile: https://soundcloud.com/dj-syclik
• LBS #91, Exploring European Football Analytics, with Max Göbel: https://learnbayesstats.com/episode/91-exploring-european-football-analytics-max-gobel/
• LBS #85, A Brief History of Sports Analytics, with Jim Albert: https://learnbayesstats.com/episode/85-brief-history-sports-analytics-jim-albert/
• Daniel about GPTs in Probabilistic Programming: https://www.youtube.com/watch?v=KUuSwLMFPHM
• LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #76, The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/
• LBS #27, Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns: https://learnbayesstats.com/episode/27-modeling-the-us-presidential-elections-with-andrew-gelman-merlin-heidemanns/
• LBS #20, Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/

Abstract

by Christoph Bamberg

Our guest this week, Daniel Lee, is a real Bayesian allrounder and will give us new insights into a lot of Bayesian applications. 

Daniel got introduced to Bayesian stats when trying to estimate the failure rate of satellite dishes as an undergraduate student. He was lucky to be mentored by Bayesian greats like David Spiegelhalter, Andrew Gelman and Bob Carpenter. He also sat in on reading groups at universities where he learned about cutting edge developments - something he would recommend anyone to really dive deep into the matter.

He used all this experience working on Pk/Pd (Pharmacokinetics/ Pharmacodynamics) models. We talk about the challenges in understanding individual responses to drugs based on the speed with which they move through the body. Bayesian statistics allows for incorporating more complexity into those models for more accurate estimation.

Daniel also worked on decision making and information fusing problems for the military, such as identifying a plane as friend or foe through the radar of several ships.

And to add even more diversity to his repertoire, Daniel now also works in the world of sports analytics, another popular topic on our show. We talk about the state of this emerging field and its challenges.

Finally, we cover some STAN news, discuss common problems and misconceptions around Bayesian statistics and how to resolve them.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
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Welcome to another installment of our LBS physics deep dive! After exploring the world of experimental physics at CERN in our first video documentary in episode 93, we’ll stay in Geneva for this one, but this time we’ll dive into theoretical physics.

We’ll explore mysterious components of the universe, like dark matter and dark energy. We’ll also see how the study of gravity intersects with the study of particle physics, especially when considering black holes and the early universe. Even crazier, we’ll see that there are actual experiments and observational projects going on to answer these fundamental questions!

Our guide for this episode is Valerie Domcke, permanent research staff member at CERN, who did her PhD in Hamburg, Germany, and postdocs in Trieste and Paris.

When she’s not trying to decipher the mysteries of the universe, Valerie can be found on boats, as she’s a big sailing fan.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls and Maksim Kuznecov.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Valerie’s webpage: https://theory.cern/roster/domcke-valerie
• Valerie on Google Scholar: https://scholar.google.com/citations?user=E3g0tn4AAAAJ
• LBS #93 A CERN Odyssey, with Kevin Greiff: https://www.youtube.com/watch?v=rOaqIIEtdpI
• LBS #64, Modeling the Climate & Gravity Waves, with Laura Mansfield: https://learnbayesstats.com/episode/64-modeling-climate-gravity-waves-laura-mansfield/
• LBS Physics Playlist: https://learnbayesstats.com/physics-astrophysics/

Abstract

by Christoph Bamberg

Episode 95 is another instalment of our Deep Dive into Physics series. And this time we move away from the empirical side of this topic towards more theoretical questions. 

There is no one better for this topic than Dr. Valerie Domcke. Valerie is the second researcher from the CERN we have on our show. She is located at the Department of Theoretical Physics there.

We mainly focus on the Standard Model of Physics, where it fails to explain observations, what proposals are discussed to update or replace it and what kind of evidence would be needed to make such a decision.

Valerie is particularly interested in situations in which the Standard Model brakes down, such as when trying to explain the excess gravitational pull observed that cannot be accounted for by visible stars. 

Of course, we cover fascinating topics like dark matter, dark energy, black holes and gravitational waves that are places to look for evidence against the Standard Model.

Looking more at the practical side of things, we discuss the challenges in disentangling signal from noise, especially in such complex fields as astro- and quantum-physics. 

We also touch upon the challenges Valerie is currently tackling in working on a new observatory for gravitational waves, the Laser Interferometer Space Antenna, LISA. 

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

In this episode, Jonathan Templin, Professor of Psychological and Quantitative Foundations at the University of Iowa, shares insights into his journey in the world of psychometrics.

Jonathan’s research focuses on diagnostic classification models — psychometric models that seek to provide multiple reliable scores from educational and psychological assessments. He also studies Bayesian statistics, as applied in psychometrics, broadly. So, naturally, we discuss the significance of psychometrics in psychological sciences, and how Bayesian methods are helpful in this field.

We also talk about challenges in choosing appropriate prior distributions, best practices for model comparison, and how you can use the Multivariate Normal distribution to infer the correlations between the predictors of your linear regressions.

This is a deep-reaching conversation that concludes with the future of Bayesian statistics in psychological, educational, and social sciences — hope you’ll enjoy it!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca and Dante Gates.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Jonathan’s website: https://jonathantemplin.com/
• Jonathan on Twitter: https://twitter.com/DrJTemplin
• Jonathan on Linkedin: https://www.linkedin.com/in/jonathan-templin-0239b07/
• Jonathan on GitHub: https://github.com/jonathantemplin
• Jonathan on Google Scholar: https://scholar.google.com/citations?user=veeVxxMAAAAJ&hl=en&authuser=1
• Jonathan on Youtube: https://www.youtube.com/channel/UC6WctsOhVfGW1D9NZUH1xFg
• Jonathan’s book: https://jonathantemplin.com/diagnostic-measurement-theory-methods-applications/
• Jonathan’s teaching: https://jonathantemplin.com/teaching/
• Vehtari et al. (2016), Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC: https://arxiv.org/abs/1507.04544
• arviz.plot_compare: https://python.arviz.org/en/stable/api/generated/arviz.plot_compare.html
• LBS #35, The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• LBS #40, Bayesian Stats for the Speech & Language Sciences, with Allison Hilger and Timo Roettger: https://learnbayesstats.com/episode/40-bayesian-stats-speech-language-sciences-allison-hilger-timo-roettger/
• Bayesian Model-Building Interface in Python: https://bambinos.github.io/bambi/

Abstract

by Christoph Bamberg

You have probably unknowingly already been exposed to this episode’s topic - psychometric testing - when taking a test at school or university. Our guest, Professor Jonathan Templin, tries to increase the meaningfulness of these tests by improving the underlying psychometric models, the bayesian way of course!

Jonathan explains that it is not easy to judge the ability of a student based on exams since they have errors and are only a snapshot. Bayesian statistics helps by naturally propagating this uncertainty to the results.

In the field of psychometric testing, Marginal Maximum Likelihood is commonly used. This approach quickly becomes unfeasible though when trying to marginalise over multidimensional test scores. Luckily, Bayesian probabilistic sampling does not suffer from this.

A further reason to prefer Bayesian statistics is that it provides a lot of information in the posterior. Imagine taking a test that tells you what profession you should pursue at the end of high school. The field with the best fit is of course interesting, but the second best fit may be as well. The posterior distribution can provide this kind of information.

After becoming convinced that Bayes is the right choice for psychometrics, we also talk about practical challenges like choosing a prior for the covariance in a multivariate normal distribution, model selection procedures and more.

In the end we learn about a great Bayesian holiday destination, so make sure to listen till the end!

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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• My Intuitive Bayes Online Courses
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This is a very special episode. It is the first-ever LBS video episode, and it takes place in the heart of particle physics research -- the CERN 🍾

I went onsite in Geneva, to visit Kevin Greif, a doctoral candidate in particle physics at UC Irvine, and we walked around the CERN campus, talking about particle physics, dark matter, dark energy, machine learning -- and a lot more!

I still released the audio form of this episode, but I really thought and made it as a video-first episode, so I strongly recommend watching this one, as you’ll get a cool tour of the CERN campus and some of its experiments ;) I put the YouTube link in the show notes.

I hope you'll enjoy this deep dive into all things physics. If you have any recommendations for other cool scientific places I should do a documentary about, please get in touch on Twitter @LearnBayesStats, or by email.

This was literally a one-person endeavor — you may have noticed that I edited the video myself. So, if you liked it, please send this episode to your friends and colleagues -- and tell them to support the show on Patreon 😉 

With enough support, that means I'll be able to continue with such in-depth content, and maybe, maybe, even pay for a professional video editor next time 🙈 

Enjoy, my dear Bayesians, and best Bayesian wishes 🖖

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Video of the episode: https://www.youtube.com/watch?v=rOaqIIEtdpI
• Kevin on Linkedin: https://www.linkedin.com/in/kevin-greif-824b091b4/ 
• Kevin on Twitter: https://twitter.com/greif_kevin
• ATLAS homepage: https://atlas.cern
• Kevin’s most recent paper on unfolding: https://arxiv.org/abs/2305.10399
• LBS on Twitter: https://twitter.com/LearnBayesStats
• LBS on Linkedin: https://www.linkedin.com/company/91594158/admin/feed/posts/
• LBS Physics Playlist: https://learnbayesstats.com/physics-astrophysics/ 
• LBS #72, Why the Universe is so Deliciously Crazy, with Daniel Whiteson: https://learnbayesstats.com/episode/72-why-the-universe-is-so-deliciously-crazy-daniel-whiteson/ 
• CERN Website: https://www.home.cern/

Some physics (and physics adjacent) books Kevin enjoys:

• Copenhagen” by Michael Frayn: https://www.amazon.com/Copenhagen-Michael-Frayn/dp/0385720793
• “The Three Body Problem” by Cixin Liu https://www.goodreads.com/book/show/20518872-the-three-body-problem
• “The Aleph” by Jorge Luis Borges https://books.google.com.ar/books/about/The_Aleph_and_Other_Stories.html?id=XYZlAAAAMAAJ&redir_esc=y
• The NOVA special that started it all for Kevin https://www.youtube.com/watch?v=V64toYdH9hU

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

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I love Bayesian modeling. Not only because it allows me to model interesting phenomena and learn about the world I live in. But because it’s part of a broader epistemological framework that confronts me with deep questions — how do you make decisions under uncertainty? How do you communicate risk and uncertainty? What does being rational even mean?

Thankfully, Gerd Gigerenzer is there to help us navigate these fascinating topics. Gerd is the Director of the Harding Center for Risk Literacy of the University of Potsdam, Germany.

Also Director emeritus at the Max Planck Institute for Human Development, he is a former Professor of Psychology at the University of Chicago and Distinguished Visiting Professor at the School of Law of the University of Virginia. 

Gerd has written numerous awarded articles and books, including Risk Savvy, Simple Heuristics That Make Us Smart, Rationality for Mortals, and How to Stay Smart in a Smart World.

As you’ll hear, Gerd has trained U.S. federal judges, German physicians, and top managers to make better decisions under uncertainty.

But Gerd is also a banjo player, has won a medal in Judo, and loves scuba diving, skiing, and, above all, reading.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
• Gerd’s website: https://www.mpib-berlin.mpg.de/staff/gerd-gigerenzer
• Do children have Bayesian intuitions: https://psycnet.apa.org/doiLanding?doi=10.1037%2Fxge0000979
• What are natural frequencies: https://www.bmj.com/content/343/bmj.d6386
• HIV screening: helping clinicians make sense of test results to patients: https://www.bmj.com/content/347/bmj.f5151
• Teaching Bayesian Reasoning in Less Than Two Hours: https://www.apa.org/pubs/journals/releases/xge-1303380.pdf
• How to Stay Smart in a Smart World – Why Human Intelligence Still Beats Algorithms: https://www.amazon.com/How-Stay-Smart-World-Intelligence/dp/0262046954
• Gut Feelings – The Intelligence of the Unconscious: https://www.amazon.com/Gut-Feelings-Intelligence-Gerd-Gigerenzer/dp/0143113763
• Better Doctors, Better Patients, Better Decisions: https://www.amazon.com/Better-Doctors-Patients-Decisions-Envisioning/dp/026251852X
• LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #87, Unlocking the Power of Bayesian Causal Inference, with Ben Vincent: https://learnbayesstats.com/episode/87-unlocking-the-power-of-bayesian-causal-inference-ben-vincent/
• As a bonus, Gerd playing the banjo: https://www.youtube.com/watch?v=qBllveuj8RI

Abstract

by Christoph Bamberg

In this episode, we have no other than Gerd Gigerenzer on the show, an expert in decision making, rationality and communicating risk and probabilities. 

Gerd is a trained psychologist and worked at a number of distinguished institutes like the Max Planck Institute for Human Development in Berlin or the University of Chicago. He is director of the Harding Center for Risk Literacy in Potsdam. 

One of his many topics of study are heuristics, a term often misunderstood, as he explains. We talk about the role of heuristics in a world of uncertainty, how it interacts with analysis and how it relates to intuition.

Another major topic of his work and this episode are natural frequencies and how they are a more natural way than conditional probabilities to express information such as the probability of having cancer after a positive screening. 

Gerd studied the usefulness of natural frequencies in practice and contributed to them being taught in high school in Bavaria, Germany, as an important tool to navigate the real world.

In general, Gerd is passionate about not only researching these topics but also seeing them applied outside of academia. He taught thousands of medical doctors how to understand and communicate statistics and also worked on a number of economical decision making scenarios.

In the end we discuss the benefits of simpler models for complex, uncertain situations, as for example in the case of predicting flu seasons.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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As you may know, I’m kind of a nerd. And I also love football — I've been a PSG fan since I’m 5 years old, so I’ve lived it all with this club.. And yet, I’ve never done a European-centered football analytics episode because, well, the US are much more advanced when it comes to sports analytics.

But today, I’m happy to say this day has come: a sports analytics episode where we can actually talk about European football. And that is thanks to Maximilan Göbel.

Max is a post-doctoral researcher in Economics and Finance at Bocconi University in Milan. Before that, he did his PhD in Economics at the Lisbon School of Economics and Management. 

Max is a very passionate football fan and played himself for almost 25 years in his local football club. Unfortunately, he had to give it up when starting his PhD — don’t worry, he still goes to the gym, or goes running and sometimes cycling.

Max is also a great cook, inspired by all kinds of Italian food, and an avid podcast listener — from financial news, to health and fitness content, and even a mysterious and entertaining Bayesian podcast…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Max’s website: https://www.maximiliangoebel.com/home
• Max on GitHub: https://github.com/maxi-tb22
• Max on LinkedIn: https://www.linkedin.com/in/maximilian-g%C3%B6bel-188b0413a/
• Max’s Soccer Analytics page: https://www.maximiliangoebel.com/soccer-analytics
• Soccer Factor Model on GitHub: https://github.com/maxi-tb22/SFM
• Max webinar on his Soccer Factor Model: https://www.youtube.com/watch?v=2dGrN8JGd_w

Max's paper using Bayesian inference:

• VARCTIC - A Baysian Vector Autoregression for the Arctic: “Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis”: https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0324.1.xml

Forecasting Arctic Sea Ice:

• Daily predictions of Arctic Sea Ice Extent: https://chairemacro.esg.uqam.ca/arctic-sea-ice-forecasting/?lang=en
• Sea Ice Outlook (SIO) Forecasting competition: https://www.arcus.org/sipn/sea-ice-outlook

Some of Max’s coauthors:

• Philippe Goulet Coulombe (UQAM): https://philippegouletcoulombe.com/
• Francis X. Diebold (UPenn): https://www.sas.upenn.edu/~fdiebold/

Abstract

by Christoph Bamberg

We already covered baseball analytics in the U.S.A. with Jim Albert in episode 85 and looked back at the decade long history of sports analytics there. How does it look like in Europe? 

To talk about this we got Max Göbel on the show. Max is a post-doctoral researcher in Economics and Finance at Bocconi University in Milan and holds a PhD in Economics from the Lisbon School of Economics and Management.

What qualifies him to talk about the sports-side of sports analytics is his passion for football and decades of playing experience. 

So, can sports analytics in Europe compete with analytics in the U.S.A.? Unfortunately, not yet. Many sports clubs do not use models in their hiring decisions, leading to suboptimal choices based on players’ reputation alone, as Max explains.

He designed a factor model for the performance of single players, borrowing from his econometrics expertise (check it out on his webpage, link in the show notes). 

We talk about how to grow this model from a simple and straight-forward Bernoulli model for the rate of scored goals to a multilevel model, incorporating other players. And of course, we discuss the benefits for using Bayesian statistics for this modelling problem.

We also cover sport analytics more generally and why it may not be so widely used in European football clubs yet. 

Besides his interest in football analytics, Max worked and works on topics in econometrics such as regression forecasting in the U.S.A., asset pricing and applying econometric methods to climate change issues like climate change forecasting and sea ice disappearance.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
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What’s the difference between MCMC and Variational Inference (VI)? Why is MCMC called an approximate method? When should we use VI instead of MCMC?

These are some of the captivating (and practical) questions we’ll tackle in this episode. I had the chance to interview Charles Margossian, a research fellow in computational mathematics at the Flatiron Institute, and a core developer of the Stan software.

Charles was born and raised in Paris, and then moved to the US to pursue a bachelor’s degree in physics at Yale university. After graduating, he worked for two years in biotech, and went on to do a PhD in statistics at Columbia University with someone named… Andrew Gelman — you may have heard of him.

Charles is also specialized in pharmacometrics and epidemiology, so we also talked about some practical applications of Bayesian methods and algorithms in these fascinating fields.

Oh, and Charles’ life doesn’t only revolve around computers: he practices ballroom dancing and pickup soccer, and used to do improvised musical comedy!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Charles’ website: https://charlesm93.github.io/
• Charles on Twitter: https://twitter.com/charlesm993
• Charles on GitHub: https://github.com/charlesm93
• Charles on Google Scholar: https://scholar.google.com/citations?user=nPtLsvIAAAAJ&hl=en
• Stan software: https://mc-stan.org/
• Torsten – Applications of Stan in Pharmacometrics: https://github.com/metrumresearchgroup/Torsten
• R̂ – Assessing the convergence of Markov chain Monte Carlo when running many short chains: https://arxiv.org/abs/2110.13017
• Revisiting the Gelman-Rubin Diagnostic: https://arxiv.org/abs/1812.09384
• An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models: https://arxiv.org/abs/2205.09059
• Pathfinder – Parallel quasi-Newton variational inference: https://arxiv.org/pdf/2108.03782.pdf
• Bayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentation/case-studies/boarding_school_case_study.html
• LBS #76 – The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/
• LBS #51 – Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• Flatiron Institute: https://www.simonsfoundation.org/flatiron/
• Simons Foundation: https://www.simonsfoundation.org/

Abstract

by Christoph Bamberg

In episode 90 we cover both methodological advances and their application, namely variational inference and MCMC sampling and their application in pharmacometrics. 

And we have just the right guest for this topic - Charles Margossian! You might know Charles from his work on STAN, his workshop teaching or his work at his current position at the Flatiron Institute.

His main focus now is on two topics: variational inference and MCMC sampling. When is variational inference (or approximate Bayesian methods) appropriate? And when does it fail? Charles answers these questions convincingly, clearing up some discussion around this topic.

In his work on MCMC, he tries to answer some fundamental questions: How much computational power should we invest? When is MCMC sampling more appropriate than approximate Bayesian methods? The short answer: when you care about quantifying uncertainty. We even talk about what the R-hat measure means and how to improve on it with nested R-hats.

After covering these two topics, we move to his practical work: pharmacometrics. For example, he worked on modelling the speed of drugs dissolving in the body or the role of genetics in the workings of drugs. 

Charles also contributes to making Bayesian methods more accessible for pharmacologists: He co-developed the Torsten library for Stan that facilitates Bayesian analysis with pharmacometric data. 

We discuss the nature of pharmacometric data and how it is usually modelled with Ordinary Differential Equations. 

In the end we briefly cover one practical example of pharmacometric modelling: the Covid-19 pandemic.

All in all, episode 90 is another detailed one, covering many state-of-the-art techniques and their application.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

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If you’ve ever tried to lose fat or gain muscle, you may have noticed… it’s not easy. But it’s precisely its complexity that makes the science of exercise and nutrition fascinating.

This is the longest LBS episode so far, and you’ll understand why pretty quickly: we covered a very wide range of topics, starting with the concept of metabolic adaptation and how our physiology and brain react to caloric deficits or caloric surpluses.

We also talked about the connection between metabolic adaptation and exercise energy compensation, shedding light on the interactions between the two, and how they make weight management more complex.

Statistics are of utmost importance in these endeavors, so of course we touched on how Bayesian stats can help mitigate the challenges of low sample sizes and over-focus on average treatment effect.

My guest for this marathon episode, is no other than Eric Trexler. Currently at the Department of Evolutionary Anthropology of Duke University, Eric conducts research on metabolism and cardiometabolic health. He has a PhD in Human Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research papers related to exercise, nutrition, and metabolism.

In addition, Eric is a former professional bodybuilder and has been coaching clients with goals related to health, fitness, and athletics since 2009.

In other words, get comfy for a broad and nerdy conversation about the mysteries related to energy expenditure regulation, weight management, and evolutionary mechanisms underpinning current health challenges.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Eric’s webpage: www.trexlerfitness.com
• Monthly Applications in Strength Sport (MASS) research review: https://massresearchreview.com/
• Eric on Twitter: https://twitter.com/EricTrexler
• Eric on Instagram: https://www.instagram.com/trexlerfitness/
• Eric on YouTube: https://www.youtube.com/@erictrexler
• Eric on Linkedin: https://www.linkedin.com/in/eric-trexler-19b8a9154/
• Eric’s research: https://www.researchgate.net/profile/Eric-Trexler
• The Metabolic Adaptation Manual – Problems, Solutions, and Life After Weight Loss: https://www.strongerbyscience.com/metabolic-adaptation/
• MASS on Instagram: https://www.instagram.com/massresearchreview/
• Burn – New Research Blows the Lid Off How We Really Burn Calories, Lose Weight, and Stay Healthy: https://www.amazon.com/Burn-Research-Really-Calories-Healthy/dp/0525541527
• Causal quartets – Different ways to attain the same average treatment effect: http://www.stat.columbia.edu/~gelman/research/unpublished/causal_quartets.pdf
• How to Change – The Science of Getting from Where You Are to Where You Want to Be: https://www.amazon.com/How-Change-Science-Getting-Where/dp/059308375X/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=
• The Sweet Spot – The Pleasures of Suffering and the Search for Meaning: https://www.amazon.com/Sweet-Spot-Pleasures-Suffering-Meaning/dp/0062910566
• The Stoic Challenge – A Philosopher's Guide to Becoming Tougher, Calmer, and More Resilient: https://www.amazon.com/Stoic-Challenge-Philosophers-Becoming-Resilient/dp/0393652491
• LBS #61 Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/

Abstract

by Christoph Bamberg

In episode 89, we cover a so-far underrepresented topic on this podcast: Nutrition science, sports science, their relation and of course, the role of Bayesian statistics in that field. 

Eric Trexler is the one introducing us to this topic. With his PhD in Human Movement Science from UNC Chapel Hill, previous career as professional bodybuilder and extensive experience as a health and fitness coach, he is perfectly suited for the job.

We cover a lot of ground in this episode, focusing on the science of weight-loss and the challenges to losing weight after a certain point due to an adapted energy expenditure. 

We look at energy expenditure and changes in metabolism from several angles, including the evolutionary background for these adaptations and how they affect us in modern times.

We also discuss how individually people react to calorie restriction or surplus, different approaches to motivate oneself to loose weight and the overall complexity of this topic.

In the later half of the episode, we focus more on the scientific practices in sports science and how they can be improved.

One way forward is, of course, to use more Bayesian statistics, especially because of the oftentimes small sample sizes in Eric’s field.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

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• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Today, we’re gonna learn about probabilistic numerics — what they are, what they are good for, and how they relate computation and inference in artificial intelligent systems.

To do this, I have the honor of hosting Philipp Hennig, a distinguished expert in this field, and the Chair for the Methods of Machine Learning at the University of Tübingen, Germany. Philipp studied in Heidelberg, also in Germany, and at Imperial College, London. Philipp received his PhD from the University of Cambridge, UK, under the supervision of David MacKay, before moving to Tübingen in 2011. 

Since his PhD, he has been interested in the connection between computation and inference. With international colleagues, he helped establish the idea of probabilistic numerics, which describes computation as Bayesian inference. His book, Probabilistic Numerics — Computation as Machine Learning, co-authored with Mike Osborne and Hans Kersting, was published by Cambridge University Press in 2022 and is also openly available online. 

So get comfy to explore the principles that underpin these algorithms, how they differ from traditional numerical methods, and how to incorporate uncertainty into the decision-making process of these algorithms.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Philipp on Twitter: https://twitter.com/PhilippHennig5
• Philipp on Github: https://github.com/philipphennig
• Philipp on LinkedIn: https://www.linkedin.com/in/philipp-hennig-635832278/
• An introductory course on Probabilistic Numerics, taught collaboratively by Philipp’s Group: https://youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s 
• An introductory tutorial on Probabilistic Numerics: https://youtu.be/0Q1ZTLHULcw 
• Philipp’s book: https://www.probabilistic-numerics.org/textbooks/
• ProbNum python package: https://probnum.readthedocs.io/en/latest/
• Probabilistic solvers for differential equations in JAX: https://pnkraemer.github.io/probdiffeq/
• Probabilistic Numerical Differential Equation Solvers in Julia: https://nathanaelbosch.github.io/ProbNumDiffEq.jl/stable/#Probabilistic-Numerical-Differential-Equation-Solvers
• Philipp’s research: https://www.probabilistic-numerics.org/
• Philipp’s academic page: https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/methods-of-machine-learning/start/ 
• Tübingen Machine Learning on YouTube: https://www.youtube.com/c/TübingenML 
• LBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt/
• LBS #12 Biostatistics and Differential Equations, with Demetri Pananos: https://learnbayesstats.com/episode/12-biostatistics-and-differential-equations-with-demetri-pananos/

Abstract

by Christoph Bamberg

In episode 88 with Philipp Henning, chair of Methods in Machine Learning at the Eberhard Karls University Tübingen, we learn about new, technical areas for the Bayesian way of thinking: Probabilistic numerics.

Philipp gives us a conceptual introduction to Machine Learning as “refining a model through data” and explains what challenges Machine Learning phases due to the intractable nature of data and the used computations. 

The Bayesian approach, emphasising uncertainty over estimates and parameters, naturally lends itself for handling these issues. 

In his research group, Philipp tries to find more general implementations of classically used algorithms, while maintaining computational efficiency. They successfully achieve this goal by bringing in the Bayesian approach to inferences. 

Philipp explains probabilistic numerics as “redescrbiing everything a computer does as Bayesian inference” and how this approach is suitable for advancing Machine Learning.

We expand on how to handle uncertainty in machine learning and Philipp details his teams approach for handling this issue.

We also collect many resources for those interested in probabilistic numerics and finally talk about the future of this field.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

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• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

I’ll be honest — this episode is long overdue. Not only because Ben Vincent is a friend, fellow PyMC Labs developer, and outstanding Bayesian modeler. But because he works on so many fascinating topics — so I’m all the happier to finally have him on the show!

In this episode, we’re gonna focus on causal inference, how it naturally extends Bayesian modeling, and how you can use the CausalPy open-source package to supercharge your Bayesian causal inference. We’ll also touch on marketing models and the pymc-marketing package, because, well, Ben does a lot of stuff ;)

Ben got his PhD in neuroscience at Sussex University, in the UK. After a postdoc at the University of Bristol, working on robots and active vision, as well as 15 years as a lecturer at the Scottish University of Dundee, he switched to the private sector, working with us full time at PyMC Labs — and that is a treat!

When he’s not working, Ben loves running 5k’s, cycling in the forest, lifting weights, and… learning about modern monetary theory.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Ben’s website: https://drbenvincent.github.io/
• Ben on GitHub: https://github.com/drbenvincent
• Ben on Twitter: https://twitter.com/inferencelab
• Ben on LinkedIn: https://www.linkedin.com/in/dr-benjamin-vincent-503571127/
• CausalPy – Causal inference for quasi-experiments: https://causalpy.readthedocs.io/en/latest/
• PyMC Marketing – Bayesian marketing toolbox in PyMC: https://www.pymc-marketing.io/en/stable/index.html
• PyMC Labs : https://www.pymc-labs.io/products/
• LBS #23 – Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit: https://learnbayesstats.com/episode/23-bayesian-stats-in-business-and-marketing-analytics-with-elea-mcdonnel-feit/
• LBS #63 – Media Mix Models & Bayes for Marketing, with Luciano Paz: https://learnbayesstats.com/episode/63-media-mix-models-bayes-marketing-luciano-paz/

Abstract

written by Christoph Bamberg

In this podcast episode, our guest, Ben Vincent, a fellow member of PyMC Labs with a PhD in Neuroscience and extensive experience in teaching and data analysis of course, introduces us to CausalPy and PyMC Marketing.

During his academic career, Ben got introduced to Bayesian statistics but, like most academics, did not come across causal inference. 

We discuss the importance of a systematic causal approach for important questions like health care interventions or marketing investments. 

Although causality is somewhat orthogonal to the choice of statistical approach, Bayesian statistics is a good basis for causal analyses, for example in the for of Directed Acyclical Graphs. 

To make causal inference more accessible, Ben developed a Python package called CausalPy, which allows you perform common causal inferences, e.g. working with natural experiments.

Ben was also involved in the development of PyMC Marketing, a package that conveniently bundles important analysis capacities for Marketing. The package focuses on Media Mix Modelling and customer lifetime analysis. 

We also talked about his extensive experience teaching statistics at university and current teaching of Bayesian methods in industry. His advice to students is to really engage with your learning material, coding through examples, making the learning more pleasurable and practical. 

Transcript

Please note that this is an automated transcript that may contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

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• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

This episode is unlike anything I’ve covered so far on the show. Let me ask you: Do you know what a research synchronous language is? What about hybrid systems? Last try: have you heard of Zelus, or ProbZelus?

If you answered “no” to one of the above, then you’re just like me! And that’s why I invited Guillaume Baudart for this episode — to teach us about all these fascinating topics!

A researcher in the PARKAS team of Inria, Guillaume's research focuses on probabilistic and reactive programming languages. In particular, he works on ProbZelus, a probabilistic extension to Zelus, itself a research synchronous language to implement hybrid systems.

To simplify, Zelus is a modeling framework to simulate the dynamics of systems both smooth and subject to discrete dynamics — if you’ve ever worked with ODEs, you may be familiar with these terms.

If you’re not — great, Guillaume will explain everything in the episode! And I know it might sound niche, but this kind of approach actually has very important applications — such as proving that there are no bugs in a program.

Guillaume did his PhD at École Normale Supérieure, in Paris, working on reactive programming languages and quasi-periodic systems. He then worked in the AI programming team of IBM Research, before coming back to the École Normale Supérieure, working mostly on reactive and probabilistic programming.

In his free time, Guillaume loves spending time with his family, playing the violin with friends, and… cooking!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Guillaume’s website: https://guillaume.baudart.eu/
• ProbZelus on GitHub: https://github.com/IBM/probzelus
• Zelus docs: https://zelus.di.ens.fr/
• Short Zelus introduction: https://www.di.ens.fr/~pouzet/bib/hscc13.pdf 
• Guillaume’s course : https://wikimpri.dptinfo.ens-cachan.fr/doku.php?id=cours:c-2-40
• LBS #74 – Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt/
• ProbZelus (design, semantics, delayed-sampling): https://dl.acm.org/doi/abs/10.1145/3385412.3386009
• Semi-symbolic inference: https://dl.acm.org/doi/abs/10.1145/3563347
• Static analysis for bounded memory inference: https://dl.acm.org/doi/abs/10.1145/3485492

Abstract

by Christoph Bamberg

Guillaume Baudart is researcher at Inria in the PARKAS team at the Département d'Informatique (DI) of the École normale supérieure. He joins us for episode 86 to tell us about ProbZelus, a synchronous probabilistic programming language, that he develops.

We have not covered synchronous languages yet, so, Guillaume gives us some context on this kind of programming approach and how ProbZelus adds probabilistic notions to it.

He explains the advantages of the probabilistic aspects of ProbZelus and what practitioners may profit from it. 

For example, synchronous languages are used to program and test autopilots of planes and ensure that they do not have any bugs. ProbZelus may be useful here as Guillaume argues.

Finally, we also touch upon his teaching work and what difficulties he encounters in teaching probabilistic programming. 

Transcript

Please note that this is an automated transcript that may contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

In this episode, I am honored to talk with a legend of sports analytics in general, and baseball analytics in particular. I am of course talking about Jim Albert.

Jim grew up in the Philadelphia area and studied statistics at Purdue University. He then spent his entire 41-year academic career at Bowling Green State University, which gave him a wide diversity of classes to teach – from intro statistics through doctoral level.

As you’ll hear, he’s always had a passion for Bayesian education, Bayesian modeling and learning about statistics through sports. I find that passion fascinating about Jim, and I suspect that’s one of the main reasons for his prolific career — really, the list of his writings and teachings is impressive; just go take a look at the show notes.

Now an Emeritus Professor of Bowling Green, Jim is retired, but still an active tennis player and writer on sports analytics — his blog, “Exploring Baseball with R”, is nearing 400 posts!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Jim’s website: https://bayesball.github.io/
• Jim’s baseball blog: https://baseballwithr.wordpress.com/
• Jim on GitHub: https://github.com/bayesball
• Jim on Twitter: https://twitter.com/albertbayes
• Jim on Linkedin: https://www.linkedin.com/in/jim-albert-22846b41/
• Jim’s baseball research: https://bayesball.github.io/BLOG/
• Probability and Bayesian Modeling book: https://monika76five.github.io/ProbBayes/
• Curve Ball -- Baseball, Statistics, and the Role of Chance in the Game: https://bayesball.github.io/curveball/curveball.htm
• Visualizing Baseball: https://bayesball.github.io/VB/
• Analyzing Baseball Data with R: https://www.amazon.com/gp/product/0815353510?pf_rd_p=c2945051-950f-485c-b4df-15aac5223b10&pf_rd_r=SFAV7QEGY9A2EDADZTJ5
• Teaching Statistics Using Baseball: https://bayesball.github.io/TSUB2/
• Ordinal Data Modeling: https://link.springer.com/book/10.1007/b98832?changeHeader
• Workshop Statistics (an intro stats course taught from a Bayesian point of view): https://bayesball.github.io/nsf_web/main.htm
• LBS #76, The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/
• MCMC Interactive Gallery: https://chi-feng.github.io/mcmc-demo/app.html?algorithm=HamiltonianMC&target=banana

Abstract

written by Christoph Bamberg

In this episode, Jim Albert, a legend of sports analytics, Emeritus Professor at Bowling Green university, is our guest.

We talk about a range of topics, including his early interest in math and sports, challenges in analysing sports data and his experience teaching statistics.

We trace back the history of baseball sport analytics to the 1960s and discuss how new, advanced ways to collect data change the possibilities of what can be modelled.

There are also statistical approaches to American football, soccer and basketball games. Jim explains why these team sports are more difficult to model than baseball. 

The conversation then turns to Jim’s substantial experience teaching statistics ad the challenges he sees in that. Jim worked on several books on sports analytics and has many blog posts on this topic.

We also touch upon the challenges of prior elicitation, a topic that has come up frequently in recent podcasts, how different stakeholders such as coaches and managers think differently about the sport and how to extract priors from their information.

For more tune in to episode 85 with Jim Albert.

Chapters

[00:00:00] Episode Begins

[00:04:04] How did you get into the world of statistics?

[00:11:17] Baseball is more advanced on the analytics path compared to other sports

[00:17:02] How is the data collected?

[00:24:43] Why is sports analytics important and is it turning humans into robots?

[00:32:51] Loss in translation problem between modellers and domain experts...?

[00:41:43] Active learning and learning through workshops

[00:51:08] Principles before methods

[00:52:30] Your favorite sports analytics model

[01:02:07] If you had unlimited time and resources which problem would you try to solve?

Transcript

Please note that this transcript is generated automatically and may contain errors. Feel free to reach out if you are willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

This is another installment in our neuroscience modeling series! This time, I talked with Konrad Kording, about the role of Bayesian stats in neuroscience and psychology, electrophysiological data to study what neurons do, and how this helps explain human behavior.

Konrad studied at ETH Zurich, then went to UC London and MIT for his postdocs. After a decade at Northwestern University, he is now Penn Integrated Knowledge Professor at the University of Pennsylvania.

As you’ll hear, Konrad is particularly interested in the question of how the brain solves the credit assignment problem and similarly how we should assign credit in the real world (through causality). Building on this, he is also interested in applications of causality in biomedical research.

And… he’s also a big hiker, skier and salsa dancer!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony and Joshua Meehl.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Konrad’s lab: https://kordinglab.com/
• Konrad’s lab on GitHub: https://github.com/KordingLab
• Konrad’s lab on Twitter: https://twitter.com/KordingLab
• LBS #81, Neuroscience of Perception: Exploring the Brain, with Alan Stocker: https://learnbayesstats.com/episode/81-neuroscience-of-perception-exploring-the-brain-alan-stocker/
• LBS #77, How a Simple Dress Helped Uncover Hidden Prejudices, with Pascal Wallisch: https://learnbayesstats.com/episode/77-how-a-simple-dress-helped-uncover-hidden-prejudices-pascal-wallisch/
• The Sports Gene, Inside the Science of Extraordinary Athletic Performance: https://davidepstein.com/david-epstein-the-sports-gene/
• Decoding with good ML: https://github.com/KordingLab/Neural_Decoding and https://www.eneuro.org/content/7/4/ENEURO.0506-19.2020
• Bayesian decoding: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5578432/
• Textbook on Bayesian modeling of behavior: bayesianmodeling.com
• Bayesian philosophy: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3981868/
• Konrad talking about Neuromatch Bayes day: https://www.youtube.com/watch?v=neDaPap_5Tg
• The Neuromatch Bayes tutorials: compneuro.neuromatch.io

Transcript

Please note that this is an automatic transcript and may contain errors. Feel free to reach out if you would like to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

One of the greatest features of this podcast, and my work in general, is that I keep getting surprised. Along the way, I keep learning, and I meet fascinating people, like Tarmo Jüristo.

Tarmo is hard to describe. These days, he’s heading an NGO called Salk, in the Baltic state called Estonia. Among other things, they are studying and forecasting elections, which is how we met and ended up collaborating with PyMC Labs, our Bayesian consultancy.

But Tarmo is much more than that. Born in 1971 in what was still the Soviet Union, he graduated in finance from Tartu University. He worked in finance and investment banking until the 2009 crisis, when he quit and started a doctorate in… cultural studies. He then went on to write for theater and TV, teaching literature, anthropology and philosophy. An avid world traveler, he also teaches kendo and Brazilian jiu-jitsu.

As you’ll hear in the episode, after lots of adventures, he established Salk, and they just used a Bayesian hierarchical model with post-stratification to forecast the results of the 2023 Estonian parliamentary elections and target the campaign efforts to specific demographics.

Oh, and let thing: Tarmo is a fan of the show — I told you he was a great guy ;)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh and Grant Pezzolesi.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Tarmo on GitHub: https://github.com/tarmojuristo
• Tarmo on Linkedin: https://www.linkedin.com/in/tarmo-j%C3%BCristo-7018bb7/
• Tarmo on Twitter: https://twitter.com/tarmojuristo
• Salk website: https://salk.ee/
• Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3I

Abstract

by Christoph Bamberg

In episode 83 of the podcast Tarmo Jüristo is our guest. He recently received media attention for his electoral forecasting in the Estonian election and potential positive role in aiding liberal parties gain more votes than expected. 

Tarmo explains to us how he used Bayesian models with his NGO SALK to forecast the election and how he leveraged these models to unify the different liberal parties that participated in the election. So, we get a firsthand view of how to use Bayesian modelling smartly.

Furthermore, we talk about when to use Bayesian models, difficulties in modelling survey data and how post-stratification can help.

He also explains how he, with the help of PyMC Labs, added Gaussian Processes to his models to better model the time-series structure of their survey data. 

We close this episode by discussing the responsibility that comes with modelling data in politics. 

Transcript

please note that the transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

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Max Kochurov’s State of Bayes Lecture Series: https://www.youtube.com/playlist?list=PL1iMFW7frOOsh5KOcfvKWM12bjh8zs9BQ

Sign up here for upcoming lessons: https://www.meetup.com/pymc-labs-online-meetup/events/293101751/

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We talk a lot about different MCMC methods on this podcast, because they are the workhorses of the Bayesian models. But other methods exist to infer the posterior distributions of your models — like Sequential Monte Carlo (SMC) for instance. You’ve never heard of SMC? Well perfect, because Nicolas Chopin is gonna tell you all about it in this episode!

A lecturer at the French university of ENSAE since 2006, Nicolas is one of the world experts on SMC. Before that, he graduated from Ecole Polytechnique and… ENSAE, where he did his PhD from 1999 to 2003.

Outside of work, Nicolas enjoys spending time with his family, practicing aikido, and reading a lot of books.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady and Kurt TeKolste.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Old episodes relevant to these topics:
• LBS #14, Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas: https://learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas/
• LBS #41, Thinking Bayes, with Allen Downey: https://learnbayesstats.com/episode/41-think-bayes-allen-downey/
• Nicolas’ show notes:
• Nicolas on Mastodon: [email protected]
• 2-hour introduction to particle filters: https://www.youtube.com/watch?v=mE_PJ9ASc8Y
• Nicolas’ website: https://nchopin.github.io/
• Nicolas on GitHub: https://github.com/nchopin
• Nicolas on Linkedin: https://www.linkedin.com/in/nicolas-chopin-442a78102/
• Nicolas’ blog (shared with others): https://statisfaction.wordpress.com/
• INLA original paper: https://people.bath.ac.uk/man54/SAMBa/ITTs/ITT2/EDF/INLARueetal2009.pdf
• Nicolas’ book, An introduction to Sequential Monte Carlo: https://nchopin.github.io/books.html
• Laplace’s Demon, A Seminar Series about Bayesian Machine Learning at Scale: https://ailab.criteo.com/laplaces-demon-bayesian-machine-learning-at-scale/
• Paper about Expectation Propagation, Leave Pima Indians Alone – Binary Regression as a Benchmark for Bayesian Computation: https://projecteuclid.org/journals/statistical-science/volume-32/issue-1/Leave-Pima-Indians-Alone--Binary-Regression-as-a-Benchmark/10.1214/16-STS581.full
• Blackjax website: https://blackjax-devs.github.io/blackjax/

Abstract

by Christoph Bamberg

In episode 82 Nicolas Chopin is our guest. He is a graduate from the Ecole Polytechnique and currently lectures at the French university of ENSAE. 

He is a specialist for Sequential Monte Carlo (SMC) samplers and explains in detail what they are, clearing up some confusion about what SMC stands for and when to use them. 

We discuss the advantages of SMC over other types of commonly used samplers for bayesian models such as MCMC or Gibbs samplers. 

Besides a detailed look at SMC we also cover INLA. INLA stands for Integrated Nested LaPlace Approximation. 

INLA can be a fast, approximate sampler for specific kinds of models. It works well for geographic data and relationships, such as for example relationships between regions in a country.

We discuss the difficulties with and future of SMC and INLA and probabilistic sampling in general.

Transcript

please note that the transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 Mentorship with me

Did you know that the way your brain perceives speed depends on your priors? And it’s not the same at night? And it’s not the same for everybody?

This is another of these episodes I love where we dive into neuroscience, how the brain works, and how it relates to Bayesian stats. It’s actually a follow-up to episode 77, where Pascal Wallisch told us how the famous black and blue dress tells a lot about our priors about how we perceive the world. So I strongly recommend listening to episode 77 first, and then come back here, to have your mind blown away again, this time by Alan Stocker.

Alan was born and raised in Switzerland. After a PhD in physics at ETH Zurich, he somehow found himself doing neuroscience, during a postdoc at NYU. And then he never stopped — still leading the Computational Perception and Cognition Laboratory of the University of Pennsylvania.

But Alan is also a man of music (playing the piano when he can), a man of coffee (he’ll never refuse an olympia cremina or a kafatek) and a man of the outdoors (he loves trashing through deep powder with his snowboard).

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady and Kurt TeKolste.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Alan’s website: https://www.sas.upenn.edu/~astocker/lab/members-files/alan.php
• Noise characteristics and prior expectations in human visual speed perception: https://www.nature.com/articles/nn1669
• Combining efficient coding with Bayesian inference as a model of human perception:
• Video: https://vimeo.com/138238753
• Paper: https://www.nature.com/articles/nn.4105
• LBS #77 How a Simple Dress Helped Uncover Hidden Prejudices, with Pascal Wallisch: https://learnbayesstats.com/episode/77-how-a-simple-dress-helped-uncover-hidden-prejudices-pascal-wallisch/
• LBS #72 Why the Universe is so Deliciously Crazy, with Daniel Whiteson: https://learnbayesstats.com/episode/72-why-the-universe-is-so-deliciously-crazy-daniel-whiteson/

Abstract

by Christoph Bamberg

In episode 81 of the podcast, Alan Stocker helps us update our priors of how the brain works. Alan, born in Switzerland, studied mechanical engineering and earned his PhD in physics before being introduced to the field of neuroscience through an internship. He is now Associate Professor at the University of Pennsylvania. 

Our conversation covers various topics related to the human brain and whether it what it does can be characterised as a Bayesian inferences. 

 Low-level visual processing, such as identifying the orientation of moving grids, can be explained with reference to Bayesian priors and updating under uncertainty. We go through several examples of this such as driving a car in foggy conditions. 

More abstract cognitive processes, such as reasoning about politics, may be more difficult to explain in Bayesian terms.

We also touch upon the question to what degree priors may be innate and how to educate people to change their priors.

In the end, Alan gives two recommendations for improving your Bayesian inferences in a political context: 1) Go out and get your own feedback and 2) try to give and receive true feedback. Listen to the episode for details.

Transcript

please note that the transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I’m sure you know at least one Bart. Maybe you’ve even used one — but you’re not proud of it, because you didn’t know what you were doing. Thankfully, in this episode, we’ll go to the roots of regression trees — oh yeah, that’s what BART stands for. What were you thinking about?

Our tree expert will be no one else than Sameer Deshpande. Sameer is an assistant professor of Statistics at the University of Wisconsin-Madison. Prior to that, he completed a postdoc at MIT and earned his Ph.D. in Statistics from UPenn.

On the methodological front, he is interested in Bayesian hierarchical modeling, regression trees, model selection, and causal inference. Much of his applied work is motivated by an interest in understanding the long-term health consequences of playing American-style tackle football. He also enjoys modeling sports data and was a finalist in the 2019 NFL Big Data Bowl.

Outside of Statistics, he enjoys cooking, making cocktails, and photography — sometimes doing all of those at the same time…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, and Arkady.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Sameer’s website: https://skdeshpande91.github.io/
• Sameer on GitHub: https://github.com/skdeshpande91
• Sameer on Twitter: https://twitter.com/skdeshpande91 
• Sameer on Google Scholar: https://scholar.google.com/citations?user=coVrnWIAAAAJ&hl=en
• LBS #50 Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #51 Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
• LBS #58 Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao: https://learnbayesstats.com/episode/58-bayesian-modeling-computation-osvaldo-martin-ravin-kumar-junpeng-lao/
• Book Bayesian Modeling and Computation in Python: https://bayesiancomputationbook.com/welcome.html
• LBS #39 Survival Models & Biostatistics for Cancer Research, with Jacki Buros: https://learnbayesstats.com/episode/39-survival-models-biostatistics-cancer-research-jacki-buros/
• Original BART paper (Chipman, George, and McCulloch 2010): https://doi.org/10.1214/09-AOAS285
• Hill (2011) on BART in causal inference: https://doi.org/10.1198/jcgs.2010.08162
• Hahn, Murray, and Carvalho on Bayesian causal forests: https://doi.org/10.1214/19-BA1195
• Main BART package in R: https://cran.r-project.org/web/packages/BART/index.html
• dbart R package: https://cran.r-project.org/web/packages/dbarts/index.html
• Sameer’s own re-implementation of BART: https://github.com/skdeshpande91/flexBART

Abstract

by Christoph Bamberg

In episode 80, Sameer Deshpande, assistant professor of Statistics at the University of Wisconsin-Madison is our guest. 

He had a passion for math from a young age. And got into Bayesian statistics at university, teaching statistics now himself. We talk about the intricacies of teaching bayesian statistics, such as helping students accept that there are no objective answers. 

Sameer’s current work focuses on Bayesian Additive Regression Trees (BARTs). He also works on prior specification, and numerous cool applied projects, for example on the effects of playing American football as an adolescent and its effects for later health

We primarily talk about BARTs as a way of approximating complex functions by using a collection of step functions. They work off the shelf pretty well and can be applied to various models such as survival models, linear models, and smooth models. BARTs are somewhat analogous to splines and can capture trajectories well over time. However, they are also a bit like a black box making them hard to interpret.

We further touch upon some of his work on practical problems, such as how cognitive processes change over time or models of baseball empires’ decision making.

Transcript

Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Decision-making and cost effectiveness analyses rarely get as important as in the health systems — where matters of life and death are not a metaphor. Bayesian statistical modeling is extremely helpful in this field, with its ability to quantify uncertainty, include domain knowledge, and incorporate causal reasoning.

Specialized in all these topics, Gianluca Baio was the person to talk to for this episode. He’ll tell us about this kind of models, and how to understand them.

Gianluca is currently the head of the department of Statistical Science at University College London. He studied Statistics and Economics at the University of Florence (Italy), and completed a PhD in Applied Statistics, again at the beautiful University of Florence.

He’s also a very skilled pizzaiolo — so now I have two reasons to come back to visit Tuscany…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, and Arkady.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Gianluca’s website: https://gianluca.statistica.it/
• Gianluca on GitHub: https://github.com/giabaio 
• Gianluca on Mastodon: https://mas.to/@gianlubaio
• Gianluca on Twitter: https://twitter.com/gianlubaio
• Gianluca on Linkedin: https://www.linkedin.com/in/gianluca-baio-b893879/
• Gianluca’s articles on arXiv: https://arxiv.org/a/baio_g_1.html
• R for Health Technology Assessment (HTA) Consortium: https://r-hta.org/ 
• LBS #50 – Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #45 – Biostats & Clinical Trial Design, with Frank Harrell: https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/
• How to find priors intuitively= https://www.youtube.com/watch?v=9shZeqKG3M0
• Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3I
• LBS Topical Playlists (also available as RSS feeds on the website): https://www.youtube.com/@learningbayesianstatistics8147/playlists

Abstract

by Christoph Bamberg

In this week’s episode, I talk to Gianluca Baio. He is the head of the department of Statistical Science at University College London and earned a MA and PhD in Florence in Statistics and Economics.

His work primarily focuses on Bayesian modeling for decision making in healthcare, for example in case studies for novel drugs and whether this alternative treatment is worth the cost. Being a relatively young field, health economics seems more open to Bayesian statistics than more established fields.

While Bayesian statistics becomes more common in clinical trial research, many regulatory bodies still prefer classical p-values. Nonetheless, a lot of COVID modelling was done using Bayesian statistics.

We also talk about the purpose of statistics, which is not to prove things but to reduce uncertainty.

Gianluca explains that proper communication is important when eliciting priors and involving people in model building. 

The future of Bayesian statistics is that statistics should have more primacy, and he hopes that statistics will stay central rather than becoming embedded in other approaches like data science, notwithstanding, communication with other disciplines is crucial.

Transcript

Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Matt Hoffman has already worked on many topics in his life – music information retrieval, speech enhancement, user behavior modeling, social network analysis, astronomy, you name it.

Obviously, picking questions for him was hard, so we ended up talking more or less freely — which is one of my favorite types of episodes, to be honest.

You’ll hear about the circumstances Matt would advise picking up Bayesian stats, generalized HMC, blocked samplers, why do the samplers he works on have food-based names, etc.

In case you don’t know him, Matt is a research scientist at Google. Before that, he did a postdoc in the Columbia Stats department, working with Andrew Gelman, and a Ph.D at Princeton, working with David Blei and Perry Cook.

Matt is probably best known for his work in approximate Bayesian inference algorithms, such as stochastic variational inference and the no-U-turn sampler, but he’s also worked on a wide range of applications, and contributed to software such as Stan and TensorFlow Probability.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode and Gabriel Stechschulte.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Matt’s website: http://matthewdhoffman.com/
• Matt on Google Scholar: https://scholar.google.com/citations?hl=en&user=IeHKeGYAAAAJ&view_op=list_works
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo: https://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf
• Tuning-Free Generalized Hamiltonian Monte Carlo: https://proceedings.mlr.press/v151/hoffman22a/hoffman22a.pdf
• Nested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chain: http://www.stat.columbia.edu/~gelman/research/unpublished/nestedRhat.pdf
• Automatic Reparameterisation of Probabilistic Programs: http://proceedings.mlr.press/v119/gorinova20a/gorinova20a.pdf

Abstract

written by Christoph Bamberg

In this episode, Matt D. Hoffman, a Google research scientist discussed his work on probabilistic sampling algorithms with me. Matt has a background in music information retrieval, speech enhancement, user behavior modeling, social network analysis, and astronomy. 

He came to machine learning (ML) and computer science through his interest in synthetic music and later took a Bayesian modeling class during his PhD. 

He mostly works on algorithms, including Markov Chain Monte Carlo (MCMC) methods that can take advantage of hardware acceleration, believing that running many small chains in parallel is better for handling autocorrelation than running a few longer chains. 

Matt is interested in Bayesian neural networks but is also skeptical about their use in practice. 

He recently contributed to a generalised Hamilton Monte Carlo (HMC) sampler, and previously worked on an alternative to the No-U-Turn-Sampler (NUTS) called MEADS. We discuss the applications for these samplers and how they differ from one another. 

In addition, Matt introduces an improved R-hat diagnostic tool, nested R-hat, that he and colleagues developed. 

Automated Transcript

Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I love dresses. Not on me, of course — I’m not nearly elegant enough to pull it off. Nevertheless, to me, dresses are one of the most elegant pieces of clothing ever invented.

And I like them even more when they change colors. Well, they don’t really change colors — it’s the way we perceive the colors that can change. You remember that dress that looked black and blue to some people, and white and gold to others? Well that’s exactly what we’ll dive into and explain in this episode.

Why do we literally see the world differently? Why does that even happen beyond our consciousness, most of the time? And cherry on the cake: how on Earth could this be related to… priors?? Yes, as in Bayesian priors!

Pascal Wallisch will shed light on all these topics in this episode. Pascal is a professor of Psychology and Data Science at New York University, where he studies a diverse range of topics including perception, cognitive diversity, the roots of disagreement and psychopathy.

Originally from Germany, Pascal did his undergraduate studies at the Free University of Berlin. He then received his PhD from the University of Chicago, where he studied visual perception.

In addition to scientific articles on psychology and neuroscience, he wrote multiple books on scientific computing and data science. As you’ll hear, Pascal is a wonderful science communicator, so it's only normal that he also writes for a general audience at Slate or the Creativity Post, and has given public talks at TedX and Think and Drink.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R and Nicolas Rode.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Pascal’s website: https://blog.pascallisch.net/about/
• Pascal on Twitter: https://twitter.com/pascallisch
• Pascal on Linkedin: https://www.linkedin.com/in/pascal-wallisch-0109b77
• “Socks & Crocs”, You Are Not So Smart podcast, Episode 200: https://youarenotsosmart.com/2021/02/22/yanss-200-how-a-divisive-photograph-of-a-perceptually-ambiguous-dress-led-two-researchers-to-build-the-nuclear-bomb-of-cognitive-science-out-of-socks-and-crocs/
• You Are Not So Smart – Live in New York at The Bell House: https://www.youtube.com/watch?v=277HGgqrrUM&t=1s
• Pascal’s paper – Illumination assumptions account for individual differences in the perceptual interpretation of a profoundly ambiguous stimulus in the color domain: https://jov.arvojournals.org/article.aspx?articleid=2617976 
• Neural Data Science – A Primer with MATLAB and Python: https://www.amazon.com/Neural-Data-Science-MATLAB%C2%AE-PythonTM/dp/0128040432
• What Color Is The Dress? The Debate That Broke The Internet: https://www.nhpr.org/2015-02-27/what-color-is-the-dress-the-debate-that-broke-the-internet#stream/0
• The inside story of the ‘white dress, blue dress’ drama that divided a planet: https://www.washingtonpost.com/news/morning-mix/wp/2015/02/27/the-inside-story-of-the-white-dress-blue-dress-drama-that-divided-a-nation/
• Noise characteristics and prior expectations in human visual speed perception: https://www.nature.com/articles/nn1669
• Bayesian integration in sensorimotor learning: https://www.nature.com/articles/nature02169

Abstract

by Christoph Bamberg

In our conversation, Pascal Wallisch, a professor of Psychology and Data Science at New York University, shared about his research on perception, cognitive diversity, the roots of disagreement, and psychopathy. 

Pascal did his undergraduate studies at the Free University of Berlin and then received his PhD from the University of Chicago, where he studied visual perception. Pascal is also a TedX, Think and Drink speaker, and writer for Slate and Creativity Post. 

We discussed Pascal's origin story, his current work on cognitive diversity, and the importance of priors in perception. 

Pascal used the example of "the Dress" picture that went viral in 2015, where people saw either black and blue or white and gold. He explained how prior experience and knowledge can affect how people perceive colors and motion, and how priors can bias people for action. 

We discussed to what extent the brain might be Bayesian and what functions are probably not so well described in bayesian terms. 

Pascal also discussed how priors can be changed through experience and exposure.

Finally, Pascal emphasized that people have different priors and perspectives, and that understanding these differences is crucial for creating a more diverse and inclusive society.

Automated Transcript

Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

How does it feel to switch careers and start a postdoc at age 47? How was it to be one of the people who created the probabilistic programming language Stan? What should the Bayesian community focus on in the coming years?

These are just a few of the questions I had for my illustrious guest in this episode — Bob Carpenter. Bob is, of course, a Stan developer, and comes from a math background, with an emphasis on logic and computer science theory. He then did his PhD in cognitive and computer sciences, at the University of Edinburgh.

He moved from a professor position at Carnegie Mellon to industry research at Bell Labs, to working with Andrew Gelman and Matt Hoffman at Columbia University. Since 2020, he's been working at Flatiron Institute, a non-profit focused on algorithms and software for science.

In his free time, Bob loves to cook, see live music, and play role playing games — think Monster of the Week, Blades in Dark, and Fate.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin and Raphaël R.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Bob’s website: https://bob-carpenter.github.io
• Bob on GitHub: https://github.com/bob-carpenter
• Bob on Google Scholar: https://scholar.google.com.au/citations?user=kPtKWAwAAAAJ&hl=en
• Stat modeling blog: https://statmodeling.stat.columbia.edu
• Stan home page: https://mc-stan.org/
• BridgeStan home page: https://github.com/roualdes/bridgestan
• bayes-infer home page: https://github.com/bob-carpenter/bayes-infer
• Crowdsourcing with item difficulty: https://github.com/bob-carpenter/rater-difficulty-paper
• Pathfinder VI system: https://www.jmlr.org/papers/v23/21-0889.html
• Flatiron Institute home page: https://www.simonsfoundation.org/flatiron/
• 0 to 100K in 10 years – Nurturing an open-source software community: https://www.youtube.com/watch?v=P9gDFHl-Hss&t=81s
• Information Theory, Inference and Learning Algorithms: https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981
• LBS #20 – Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari: https://learnbayesstats.com/episode/20-regression-and-other-stories-with-andrew-gelman-jennifer-hill-aki-vehtari/
• LBS #27 – Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns: https://learnbayesstats.com/episode/27-modeling-the-us-presidential-elections-with-andrew-gelman-merlin-heidemanns/
• LBS #17 – Reparametrize Your Models Automatically, with Maria Gorinova: https://learnbayesstats.com/episode/17-reparametrize-your-models-automatically-with-maria-gorinova/
• LBS #36 – Bayesian Non-Parametrics & Developing Turing.jl, with Martin Trapp: https://learnbayesstats.com/episode/36-bayesian-non-parametrics-developing-turing-julia-martin-trapp/
• LBS #19 – Turing, Julia and Bayes in Economics, with Cameron Pfiffer: https://learnbayesstats.com/episode/19-turing-julia-and-bayes-in-economics-with-cameron-pfiffer/
• LBS #74 – Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt: https://learnbayesstats.com/episode/74-optimizing-nuts-developing-zerosumnormal-distribution-adrian-seyboldt/
• Bayesian Workflow paper: https://arxiv.org/abs/2011.01808
• BAyesian Model-Building Interface (Bambi) in Python: https://bambinos.github.io/bambi/
• On Being Certain: Believing You Are Right Even When You're Not: https://www.amazon.com/Being-Certain-Believing-Right-Youre/dp/031254152X

Abstract

by Christoph Bamberg

In this episode, you meet the man behind the code. Namely, Bob Carpenter, one of the core developers of STAN, a popular statistical programming language. 

After working in computational linguistic for some time, Bob became a PostDoc with Andrew Gellman to really learn Statistics and Modelling.

There he and a small team developed the first implementation of STAN. We talk about the challenges associated with the team growing and the Open Source conventions. 

Besides the initial intention behind and the beginning of STAN, we talk about the future of probabilistic programming.

Creating a tool for people with different degrees of mathematics and programming knowledge is a big challenge and working with these tools may also be more difficult for the user.

We discuss why Bayesian statistical programming is popular nonetheless and what makes it uniquely adequate for research.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

If you’re a nerd like me, you’re always curious about the physics of any situation. So, obviously, when I watched Top Gun 2, I became fascinated by the aerodynamics of fighters jets. And it so happens that one of my friends used to be a fighter pilot for the Canadian army… Immediately, I thought this would make for a cool episode — and here we are!

Actually, Jason Berndt wanted to be a pilot from the age of 3. When he was 6, he went to an air show, and then specifically wanted to become a fighter pilot. In his teens, he learned how to fly saliplanes, small single engine aircrafts. At age 22, he got a bachelor’s in aero engineering from the royal military college, and then — well, he’ll tell you the rest in the episode.

Now in his thirties, he owns real estate and created his own company, My Two Brows, selling temporary eyebrow tattoos — which, weirdly enough, is actually related to his time in the army…

In his free time, Jason plays the guitar, travels around the world (that’s actually how we met), and loves chasing adrenaline however he can (paragliding, scuba diving, you name it!).

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin and Raphaël R.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• My Two Brows website: https://mytwobrows.com/
• My Two Brows on Instagram: https://www.instagram.com/my_two_brows/
• My Two Brows on YouTube: https://www.youtube.com/channel/UC6eQgQ4qoGE2RStDJkumUGg
• PyMC Labs Workshop – Hierarchical Bayesian Modeling of Survey Data with Post-stratification: https://www.youtube.com/watch?v=efID35XUQ3I

Abstract

written by Christoph Bamberg

In this episode of the Learning bayesian statistics podcast we do not talk about Bayesianism, let alone statistics. Instead we dive into the world of fighter jets and Top Gun pilots with Jason Berndt. 

Jason is a former fighter jet pilot turned entrepreneur. He looks back at his time as a pilot, how he got there, the challenges and thrills of this job and how it influences him now in his new life. 

We also touch upon physics and science related aspects like G-force, centrifugal power, automation in critical environments like flying a fighter jet and human-computer interaction.

Jason discusses the recent movie Top Gun: Maverick and how realistic the flying was as well as the description of the fighter pilots’ lives.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

We need to talk. I had trouble writing this introduction. Not because I didn’t know what to say (that’s hardly ever an issue for me), but because a conversation with Adrian Seyboldt always takes deliciously unexpected turns.

Adrian is one of the most brilliant, interesting and open-minded person I know. It turns out he’s courageous too: although he’s not a fan of public speaking, he accepted my invitation on this show — and I’m really glad he did!

Adrian studied math and bioinformatics in Germany and now lives in the US, where he enjoys doing maths, baking bread and hiking.

We talked about the why and how of his new project, Nutpie, a more efficient implementation of the NUTS sampler in Rust. We also dived deep into the new ZeroSumNormal distribution he created and that’s available from PyMC 4.2 onwards — what is it? Why would you use it? And when?

Adrian will also tell us about his favorite type of models, as well as what he currently sees as the biggest hurdles in the Bayesian workflow.

Each time I talk with Adrian, I learn a lot and am filled with enthusiasm — and now I hope you will too!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey and Andreas Kröpelin.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• LBS on Twitter: https://twitter.com/LearnBayesStats
• LBS on Linkedin: https://www.linkedin.com/company/learn-bayes-stats/
• Adrian on GitHub: https://github.com/aseyboldt
• Nutpie repository: https://github.com/pymc-devs/nutpie
• ZeroSumNormal distribution: https://www.pymc.io/projects/docs/en/stable/api/distributions/generated/pymc.ZeroSumNormal.html
• Pathfinder – A parallel quasi-Newton algorithm for reaching regions of high probability mass: https://statmodeling.stat.columbia.edu/2021/08/10/pathfinder-a-parallel-quasi-newton-algorithm-for-reaching-regions-of-high-probability-mass/

Abstract

by Christoph Bamberg

Adrian Seyboldt, the guest of this week’s episode, is an active developer of the PyMC library in Python and his new tool nutpie in Rust. He is also a colleague at PyMC-Labs and friend. So naturally, this episode gets technical and nerdy. 

We talk about parametrisation, a topic important for anyone trying to implement a Bayesian model and what to do or avoid (don't use the mean of the data!). 

Adrian explains a new approach to setting categorical parameters, using the Zero Sum Normal Distribution that he developed. The approach is explained in an accessible way with examples, so everyone can understand and implement it themselves.

We also talked about further technical topics like initialising a sampler, the use of warm-up samples, mass matrix adaptation and much more. The difference between probability theory and statistics as well as his view on the challenges in Bayesian statistics complete the episode. 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I’m guessing you already tried to communicate the results of a statistical model to non-stats people — it’s hard, right? I’ll be honest: sometimes, I even prefer to take notes during meetings than doing that… But shhh, that’s out secret.

But all of this was before. Before I talked with Jessica Hullman. Jessica is the Ginny Rometty associate professor of computer science at Northwestern University.

Her work revolves around how to design interfaces to help people draw inductive inferences from data. Her research has explored how to best align data-driven interfaces and representations of uncertainty with human reasoning capabilities, which is what we’ll mainly talk about in this episode.

Jessica also tries to understand the role of interactive analysis across different stages of a statistical workflow, and how to evaluate data visualization interfaces.

Her work has been awarded with multiple best paper and honorable mention awards, and she frequently speaks and blogs on topics related to visualization and reasoning about uncertainty — as usual, you’ll find the links in the show notes.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox and Trey Causey.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

General links from the show:

• Jessica’s website: http://users.eecs.northwestern.edu/~jhullman/ 
• Jessica on Twitter: https://twitter.com/JessicaHullman
• Midwest Uncertainty Collective: https://mucollective.northwestern.edu/
• Jessica’s posts on Andrew Gelman’s blog: https://statmodeling.stat.columbia.edu/
• Jessica’s posts on Medium: https://medium.com/multiple-views-visualization-research-explained
• LBS # 66, Uncertainty Visualization & Usable Stats, with Matthew Kay: https://learnbayesstats.com/episode/66-uncertainty-visualization-usable-stats-matthew-kay/

Some of Jessica’s research that she mentioned:

• A Bayesian Model of Cognition to Improve Data Visualization: https://mucollective.northwestern.edu/files/2019-BayesianVis-CHI.pdf
• Visual Reasoning Strategies for Effect Size Judgments and Decisions: https://mucollective.northwestern.edu/files/2020%20-%20Kale,%20Visual%20Reasoning%20Strategies%20for%20Effect%20Size%20Judgements.pdf
• Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data: https://mucollective.northwestern.edu/files/2018-HOPsTrends-InfoVis.pdf

Behavioral economics paper Jessica mentioned:

• A Model of Non-belief in the Law of Large Numbers: https://scholar.harvard.edu/files/rabin/files/barney2014.pdf

More on David Blackwell:

• Summary of his career: https://stat.illinois.edu/news/2020-07-17/david-h-blackwell-profile-inspiration-and-perseverance
• His original work on Blackwell ordering: https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-24/issue-2/Equivalent-Comparisons-of-Experiments/10.1214/aoms/1177729032.pdf
• Lectures on day 5 of this workshop covered his work on approachability: https://old.simons.berkeley.edu/workshops/schedule/16924

Abstract:

by Christoph Bamberg

Professor Jessica Hullman from Northwestern University is an expert in designing visualisations that help people learn from data and not fall prey to biases.

She focuses on the proper communication of uncertainty, both theoretically and empirically.

She addresses questions like “Can a Bayesian model of reasoning explain apparently biased reasoning?”, “What kind of visualisation guides readers best to a valid inference?”, “How can biased reasoning be so prevalent - are there scenarios where not following the canonical reasoning steps is optimal?”.

In this episode we talk about her experimental studies on communication of uncertainty through visualisation, in what scenarios it may not be optimal to focus too much on uncertainty and how we can design models of reasoning that can explain actual behaviour and not discard it as biased. 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

What happens inside a black hole? Can we travel back in time? Why is the Universe even here? This is the type of chill questions that we’re all asking ourselves from time to time — you know, when we’re sitting on the beach.

This is also the kind of questions Daniel Whiteson loves to talk about in his podcast, “Daniel and Jorge Explain the Universe”, co-hosted with Jorge Cham, the author of PhD comics. Honestly, it’s one of my favorite shows ever, so I warmly recommend it. Actually, if you’ve ever hung out with me in person, there is a high chance I started nerding out about it…

Daniel is, of course, a professor of physics, at the University of California, Irvine, and also a researcher at CERN, using the Large Hadron Collider to search for exotic new particles — yes, these are particles that put little umbrellas in their drinks and taste like coconut.

On his free time, Daniel loves reading, sailing and baking — I can confirm that he makes a killer Nutella roll!

Oh, I almost forgot: Daniel and Jorge wrote two books — We Have No Idea and FAQ about the Universe — which, again, I strongly recommend. They are among my all-time favorites.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• PyMC Labs Meetup, Dec 8th 2022, A Candle in the Dark – How to Use Hierarchical Post-Stratification with Noisy Data: https://www.meetup.com/pymc-labs-online-meetup/events/289949398/
• Daniel’s website: https://sites.uci.edu/daniel/
• Daniel on Twitter: https://twitter.com/DanielWhiteson
• “Daniel and Jorge Explain the Universe”: https://sites.uci.edu/danielandjorge/?pname=danielandjorge.com&sc=dnsredirect
• We Have No Idea – A Guide To The Unknown Universe: https://phdcomics.com/noidea/
• Frequently Asked Questions About The Universe: https://sites.uci.edu/universefaq/
• Learning to Identify Semi-Visible Jets: https://arxiv.org/abs/2208.10062
• Twitter thread about the paper above: https://twitter.com/DanielWhiteson/status/1561929005653057536

Abstract

by Christoph Bamberg

Big questions are tackled in episode 72 of the Learning Bayesian Statistics Podcast: “What is the nature of the universe?”, “What is the role of science?”, “How are findings in physics created and communicated?”, “What is randomness actually?”. This episode’s guest, Daniel Whitesun, is just the right person to address these questions.

He is well-known for his own podcast “Daniel and Jorge Explain the Universe”, wrote several popular science books on physics and works as a particle physicist with data from the particle physics laboratory CERN.

He manages to make sense of Astrology, although he is not much of a star-gazer himself. Daniel prefers to look for weird stuff in the data of colliding particles and ask unexpected questions.

This comes with great statistical challenges that he tackles with Bayesian statistics and machine learning, while he also subscribes to the frequentist philosophy of statistics.

In the episode, Alex and Daniel touch upon many of the great ideas in quantum physics, the Higgs boson, Schrödinger’s cat, John Bell’s quantum entanglement discoveries, true random processes and much more. Mixed in throughout are pieces of advice for anyone scientifically-minded and curious about the universe.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

This episode will show you different sides of the tech world. The one where you research and apply algorithms, where you get super excited about image recognition and AI-generated art. And the one where you support social change actors — aka the “AI for Good” movement.

My guest for this episode is, quite naturally, Julien Cornebise. Julien is an Honorary Associate Professor at UCL. He was an early researcher at DeepMind where he designed its early algorithms. He then worked as a Director of Research at ElementAI, where he built and led the London office and “AI for Good” unit.

After his theoretical work on Bayesian methods, he had the privilege to work with the NHS to diagnose eye diseases; with Amnesty International to quantify abuse on Twitter and find destroyed villages in Darfur; with Forensic Architecture to identify teargas canisters used against civilians.

Other than that, Julien is an avid reader, and loves dark humor and picking up his son from school at the 'hour of the daddies and the mommies”.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek and Paul Cox.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Julien’s website: https://cornebise.com/julien/
• Julien on Twitter: https://twitter.com/JCornebise
• Julien on LinkedIn: https://www.linkedin.com/in/juliencornebise/ 
• Julien on Scholar: https://scholar.google.co.uk/citations?user=6fkVVz4AAAAJ&hl=en&oi=ao
• Stable Diffusion is a really big deal: https://simonwillison.net/2022/Aug/29/stable-diffusion/
• LBS #21, Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova/
• pymc.find_constrained_prior function: https://www.pymc.io/projects/docs/en/stable/api/generated/pymc.find_constrained_prior.html#pymc.find_constrained_prior
• LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter/
• LBS #67 Exoplanets, Cool Worlds & Life in the Universe, with David Kipping: https://learnbayesstats.com/episode/67-exoplanets-cool-worlds-life-in-universe-david-kipping/

Abstract

by Christoph Bamberg

Julien Cornebise goes on a deep dive into deep learning with us in episode 71. He calls himself a “passionate, impact-driven scientist in Machine Learning and Artificial Intelligence”. He holds an Honorary Associate Professor position at UCL, was an early researcher at DeepMind, went on to become Director of Research at ElementAI and worked with institutions ranging from the NHS in Great-Britain to Amnesty International.

He is a strong advocate for using Artificial Intelligence and computer engineering tools for good and cautions us to think carefully about who we develop models and tools for. Ask the question: What could go wrong? How could this be misused? The list of projects where he used his computing skills for good is long and divers: With the NHS he developed methods to measure and diagnose eye diseases. For Amnesty International he helped quantify the abuse female journalists receive on Twitter, based on a database of tweets labeled by volunteers.

Beyond these applied projects, Julien and Alex muse about the future of structured models in times of more and more popular deep learning approaches and the fascinating potential of these new approaches. He advices anyone interested in these topics to be comfortable with experimenting by themselves and potentially breaking things in a non-consequential environment.

And don’t be too intimidated by more seasoned professionals, he adds, because they probably have imposter-syndrome themselves which is a sign of being aware of ones own limitations. 

Automated Transcript

Please note that the following transcript was generated automatically and may therefore contain errors. Feel free to reach out if you’re willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Back in 2016, when I started dedicating my evenings and weekends to learning how to code and do serious stats, I was a bit lost… Where do I start? Which language do I pick? Why are all those languages just named with one single letter??

Then I found some stats classes by Justin Bois — and it was a tremendous help to get out of that wood (and yes, this was a pun). I really loved Justin’s teaching because he was making the assumptions explicit, and also explained them — which was so much more satisfying to my nerdy brain, which always wonders why we’re making this assumption and not that one.

So of course, I’m thrilled to be hosting Justin on the show today! Justin is a Teaching Professor in the Division of Biology and Biological Engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA, as well as the Max Planck Institute in Dresden, Germany.

Most importantly for the football fans, he’s a goalkeeper — actually, the day before recording, he saved two penalty kicks… and even scored a goal! A big fan of Los Angeles football club, Justin is a also a magic enthusiast — he is indeed a member of the Magic Castle…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken and Or Duek.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Justin’s website: http://bois.caltech.edu/index.html 
• Justin on GitHub: https://github.com/justinbois/
• Justin’s course on Data analysis with frequentist inference: https://bebi103a.github.io/
• Justin’s course on Bayesian inference: https://bebi103b.github.io/
• LBS #6, A principled Bayesian workflow, with Michael Betancourt:  https://learnbayesstats.com/episode/6-a-principled-bayesian-workflow-with-michael-betancourt/
• Physical Biology of the Cell: https://www.routledge.com/Physical-Biology-of-the-Cell/Phillips-Kondev-Theriot-Garcia-Phillips-Kondev-Theriot-Garcia/p/book/9780815344506
• Knowledge Illusion – Why We Never Think Alone: https://www.amazon.fr/Knowledge-Illusion-Never-Think-Alone/dp/039918435XThe
• Sustainable Energy – Without the Hot Air: https://www.amazon.com/Sustainable-Energy-Without-Hot-Air/dp/0954452933
• Information Theory, Inference and Learning Algorithms: https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981

Abstract

By Christoph Bamberg

Justin Bois did his Bachelor and PhD in Chemical Engineering before working as a Postdoctoral Researcher in Biological Physics, Chemistry and Biological Engineering. He now works as a Teaching Professor at the division of Biology and Biological Engineering at Caltech, USA.

He first got into Bayesian Statistics like many scientists in fields like biology or psychology, by wanting to understand what the statistics actually mean that he was using. His central question was “what is parameter estimation actually?”. After all, that’s a lot of what doing quantitative science is on a daily basis!

The Bayesian framework allowed him to find an answer and made him feel like a more complete scientist. As a teaching professor, he is now helping students of life sciences such as neuroscience or biological engineering to become true Bayesians.

His teaching covers what you need to become a proficient Bayesian analyst, from opening datasets to Bayesian inference. He emphasizes the importance of models implicit in quantitative research and shows that we do in most cases have a prior idea of an estimand’s magnitude.

Justin believes that we are naturally programmed to think in a Bayesian framework but still should mess up sometimes to learn that statistical techniques are fragile. You can find some of his teaching on his website.

Transcript

This transcript was generated automatically. Some transcription errors may have remained. Feel free to reach out if you're willing to correct them.

[00:00:00] In 2016, when I started dedicating my evenings and weekends to learning how to code and do serious stats, I was a bit lost, to be honest. Where do I start? Which language do I speak? Why are all those languages just named with one single letter, like R or C? Then I found some stats classes by just in voice.

And it was a tremendous help to get out of that wood. And yes, this was a pun. I really enjoyed Justine's teaching because he was making the assumptions explicit, and he also explained them, which was so much more satisfying to my minority brain, which always wonders why we're making this assumption and not that one.

So of course, I'm thrilled to be hosting Justin on the show today. Justin is a teaching professor in the division of biology and biological engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA as well as the Max Plan Institute in Tris, Germany.

Most importantly, for the football fans, Justin is a goalkeeper. [00:01:00] Actually, the day before recording, he saved two penalty, penalty, kicks, and even scored a goal. Yes, a big fan of Los Angeles's football club. Justine is also a magic enthusiast. He is indeed a member of the Magic Castle. This is Learning Patient Statistics.

Ex episode 70, recorded September 2nd, 2022. Welcome to Learning Patient Statistics, a fortnightly podcast on Beijing Inference, The methods project in the People who Make Impossible. I'm your host, Alex Andora. You can follow me Twitter at ann underscore like the country. For any info about the podcast, learn base stats.com is lap less to be Show notes becoming corporate sponsor supporting lbs and Pat.

Unlocking base merge, everything is in there. That's learn base dance.com. If with all that info, a model is still resisting you, or if you find my voice special, smooth and [00:02:00] want me to come and teach patient stats in company, then reach out at [email protected] or book call with me at learnbayesstats.com.

Thanks a lot folks. And best patient wish shes to you old. Let me show you how to be a good bla and change your predictions after taking information and, and if you're thinking they'll be less than amazing, let's adjust those expectations. What's a basian is someone who cares about evidence and doesn't jump to assumptions based on intuitions and prejudice.

Abassian makes predictions on the best available info and adjusts the probability cuz every belief is provisional. And when I kick a flow, mostly I'm watching eyes widen. Maybe cuz my likeness lowers expectations of tight ryman. How would I know unless I'm Ryman in front of a bunch of blind men, drop in placebo controlled science like I'm Richard Feinman, just in boys.

Welcome to Learning Patient St Sticks. Thank you. Happy to be here. Yes. Very [00:03:00] happy to have you here because, well, you know that, but listeners do not. But you are actually one of the first people who introduced me back to, uh, statistics and programming in 2017 when I started my Carrie Shift. So it's awesome to have you here today.

I'm glad my stuff helped you get going. That's, that's the point. That's the goal. Yeah. Yeah, that's really cool. And also, I'm happy to have learned how you pronounce your last name because in French, you know, that's a French name. I dunno if you have some French origin, but in French it means, I know, I know it's a French name, but it's actually, as far as I understand, my family's from Northern Germany and there's a, a name there that's spelled b e u s s, like, and it's pronounced like in Germany, you say Boce.

And then it got anglicized, I think when I moved to the US but uh, I was actually recently, just this past summer in Luanne, Switzerland, and there was a giant wood recycling bin. With my name on it, , it said d i s. So I got my picture taken next to that. So yeah. Yeah. Lo Zen is in the French speaking part of Switzerland.[00:04:00]

That's right. Cool. So we're starting already with the origin story, so I love that cuz it's actually always my first question. So how did you jump to the stats in biology worlds and like how Senior of a Pass read it? Well, I think the path that I had toward really thinking carefully about statistical inferences is a very common path among scientists, meaning scientists outside of data scientists and, and maybe also outside of really data rich branches of sciences such as astronomy.

So I studied chemical engineering as an undergraduate. It was a standard program. I didn't really do any undergrad research or anything, but I got into a little bit of statistics when I had a job at Kraft Foods. After undergraduate where I worked at the statistician on doing some predictive modeling about, uh, some food safety issues.

And I thought it was interesting, but I sort of just, I was an engineer. I was making the product, I was implementing the stuff in the production facility and the statistician kind of took care of [00:05:00] everything else. I thought, I thought he was one of the coolest people in the company, . Um, but I didn't really, you know, it didn't really hook me in to really thinking about that.

But I went and did a PhD and my PhD really didn't involve really much experimentation at all. I was actually doing computational modeling of like how nucleic acids get their structure and shape and things. And that was, it just didn't really involve analysis of much data. Then in my post-doctoral studies, in my post-doctoral work, I was working with some experimentalists who had some data sets and they needed.

do estimates of parameters based on some theoretical models that I had derived or worked on. And I had done some stuff and you know, various lab classes and stuff, but it's your standard thing. It's like, ooh, I know how to do a cur fit. Meaning I can, I guess in the Python way I would do it, SciPi dot optimized dot cur fit.

Or you know, in MATLAB I could do at least squares or something like that. And, and I knew this idea of minimizing the sum of the square of the residuals and that's gonna get you [00:06:00] a line that looks close to what your data points are. But the inference problems, the theoretical curves were actually a little bit say for some of 'em.

There was no close to form solution. They were actually solutions to differential equations. And so the actual theoretical treatment I had was a little bit more complicated. And so I needed to start to think a little bit more carefully about exactly how we're going about estimating the parameters thereof.

Right? And so I kind of just started grabbing uh, books and I. Discovered quickly that I had no idea what I was doing, , and actually neither did anybody around me. And I don't mean that pejoratively, it's just, it's a very common thing among the scient. A lot of people in the sciences that aren't, that don't work as much with data.

And perhaps it's less common now, but it's definitely more common than, you know, 10, 15, uh, years ago. And so I just kind of started looking into how we should actually think about the estimates of [00:07:00] parameters given a data set. And really what happened was the problem became crystallized for me, the problem of parameter estimation.

And I had never actually heard that phrase, perimeter estimation. To me. It was find the best fit per. If your curve goes through your data point, that means that you're, the theory that you derived is probably pretty good. And of course, I didn't think about what the word probably meant there. I, I only knew it colloquially, right?

And so, cuz I was focused on deriving what the theory is. And of course that's a whole, hugely important part of, of the scientific enterprise. But once you get that theory arrived to try to estimate the parameters of that are present in that theory from measurement, that problem just became clear to me.

Once I had a clear problem statement, then I was able to start to think about how to solve it. And so the problem statement was, I have a theory that has a set of parameters. I want to try to figure out what the parameters are by taking [00:08:00] some measurements and checking for one set of parameters. The measurements would be different.

How do I find what parameters there are to, to give me this type, type of data that I observe. I intentionally just stated that awkwardly because that awkwardness there sort of made the, It's funny, it made it clear to me that the problem was unclear . And, and so I, that's what got me into a basian mode of thinking because it was hard for me to wrap my head around what it meant to do that thing that I've been doing all this time.

This minimizing some squares of residuals and trying to find the best fit parameter. And, you know, in retrospect now I've actually, you know, that I taught myself. Cause I didn't really ever have a course in statistical inference or anything like that, say Okay. I was essentially doing a maximum likelihood estimation, which is a f way of doing prime destination.

And I, and I hadn't actually thought about what that meant. I mean, I understand that now. We don't really need to talk [00:09:00] about that since we're talking about BA stuff now, but, and it was just harder for me to wrap my head around what that meant. And so I started reading. About the basing interpretation of probability, and it was really, it really just crystallized everything and made it clear, and then I could state the problem much more clearly.

The problem was I was trying to find a posterior probability density function for these parameters given the data, and that was just so much clearly stated in Baying framework, and then that kinda lit me on fire because I was like, Holy cow, this thing that we do so often in the scientific enterprise, I can actually state the question , right?

And I just thought that was such a profound moment, and then I was kind of hooked from there on out and I, I was concent trying to improve how I thought about these things. And yeah, so I did a lot of reading. I realized I just talked a lot. You probably have [00:10:00] some questions about some of the stuff I just said, so please.

Oh yeah, well wait. But, um, I mean, that's good to have a, an overview like that. And so I guess that's also like, it sounds like you were introduced to patient statistics at the same time as you were doing that deep dive into, wait, like, I'm not sure I understand what I'm using then. Oh, actually I don't understand anything and then I have to learn about that.

But it seems that you, you were also introduced to patient stats at that same time, Is that right? Yeah, I think so. And I think this is actually sort of a classic way in which scientists come up with what it is that they want to study. Because instead you start poking around, you kind of don't really know where the holes in your knowledge are.

And so what I saw was like just a giant hole in my knowledge and my toolbox, and I saw the hole and I said, All right, let's fill it . And um, and so then I just started feeling around on how to do that. I see. And I am also curious as [00:11:00] to, and what motivated you to dive into the Beijing way of doing things?

I really do think it was the clarity. I think that, Okay. I think that arguing about like what interpretation or probability you wanna use is not the most fruitful way to spend one's time. For me, it was really, it was just so much more intuitive. I felt like I could have this interpretation of probability that it's, it's a quantification of the plausibility of a logical conjecture of any logical conjecture gave me sort of the flexibility where I could think about like a...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

A great franchise comes with a great rivalry: Marvel has Iron Man and Captain America; physics has General Relativity and Quantum Physics; and Bayesian stats has Posterior Estimation and… Bayes Factors!

A few months ago, I had the pleasure of hosting EJ Wagenmakers, to talk about these topics. This time, I’m talking with Jorge Tendeiro, who has a different perspective on Null Hypothesis Testing in the Bayesian framework, and its relationship with generative models and posterior estimation.

But this is not your classic, click-baity podcast, and I’m not interested in pitching people against each other. Instead, you’ll hear Jorge talk about the other perspective fairly, before even giving his take on the topic. Jorge will also tell us about the difficulty of arguing through papers, and all the nuances you lose compared to casual discussions.

But who is Jorge Tendeiro? He is a professor at Hiroshima University in Japan, and he was recommended to me by Pablo Bernabeu, a listener of this very podcast.

Before moving to Japan, Jorge studied math and applied stats at the University of Porto, and did his PhD in the Netherlands. He focuses on item response theory (specifically person fit analysis), and, of course, Bayesian statistics, mostly Bayes factors.

He’s also passionate about privacy issues in the 21st century, an avid Linux user since 2006, and is trying to get the hang of the Japanese language.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas and Robert Yolken.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Jorge’s website: https://www.jorgetendeiro.com/
• Jorge on Twitter: https://twitter.com/jntendeiro
• Jorge on GitHub: https://github.com/jorgetendeiro
• A Review of Issues About Null Hypothesis Bayesian Testing:  https://pure.rug.nl/ws/portalfiles/portal/159021509/2019_26880_001.pdf
• Advantages Masquerading as ‘Issues’ in Bayesian Hypothesis Testing – A Commentary on Tendeiro and Kiers: https://psyarxiv.com/nf7rp
• On the white, the black, and the many shades of gray in between – Our reply to van Ravenzwaaij and Wagenmakers: https://psyarxiv.com/tjxvz/
• LBS #61, Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
• LBS #67, Exoplanets, Cool Worlds & Life in the Universe, with David Kipping: https://learnbayesstats.com/episode/67-exoplanets-cool-worlds-life-in-universe-david-kipping/

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Hosting someone like Kevin Murphy on your podcast is… complicated. Not because Kevin himself is complicated (he’s delightful, don’t make me say what I didn’t say!), but because all the questions I had for him amounted to a 12-hour show.

Sooooo, brace yourselves folks!

No, I'm kidding. Of course I didn’t do that folks, Kevin has a life! This life started in Ireland, where he was born. He grew up in England and got his BA from the University of Cambridge. After his PhD at UC Berkeley, he did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California in 2011 on his sabbatical and then ended up staying. 

He currently runs a team of about 8 researchers inside of Google Brain working on generative models, optimization, and other, as Kevin puts it, “basic” research topics in AI/ML. He has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and the last one coming in 2023. You may be familiar with his 2012 book, as it was awarded the DeGroot Prize for best book in the field of statistical science.

Outside of work, Kevin enjoys traveling, outdoor sports (especially tennis, snowboarding and scuba diving), as well as reading, cooking, and spending time with his family.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Kevin’s website: https://www.cs.ubc.ca/~murphyk/
• Kevin on Twitter: https://mobile.twitter.com/sirbayes
• Kevin’s books (free pdf) on GitHub (includes a link to places where you can buy the hard copy): https://probml.github.io/pml-book/
• Book that inspired Kevin to get into AI: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
• State-space models library in JAX (WIP): https://github.com/probml/ssm-jax
• Other software for the book (also in JAX): https://github.com/probml/pyprobml
• Fun photo of Kevin’s hacked-up wearable camera system from 20 years ago: https://www.cs.ubc.ca/~murphyk/Vision/placeRecognition.html

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Is there life in the Universe? It doesn’t get deeper than this, does it? And yet, why do we care about that? In the very small chance that there is other life in the Universe, we have even less chance to discover it, talk to it and meet it. So, why do we care?

Well, it may surprise you but Bayesian statistics helps us think about these astronomical and — dare I say? — philosophical topics, as my guest, David Kipping, will brilliantly explain in this episode.

David is an Associate Professor of Astronomy at Columbia University, where he leads the Cool Worlds Lab — I know, the name is awesome. His team’s research spans exoplanet discovery and characterization, the search for life in the Universe and developing novel approaches to our exploration of the cosmos.

David also teaches astrostatistics, and his contributions to Bayesian statistics span astrobiology to exoplanet detection. He also hosts the Cool Worlds YouTube channel, with over half a million subscribers, that discusses his team’s work and broader topics within the field.

Cool worlds, cool guest, cool episode.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha, Scott Anthony Robson, David Haas and Robert Yolken.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• David’s website: http://user.astro.columbia.edu/~dkipping/
• David on Twitter: https://twitter.com/david_kipping
• David’s YouTube channel: https://www.youtube.com/c/coolworldslab
• David’s research group: https://www.coolworldslab.com/
• Bayesian analysis of the astrobiological implications of life’s early emergence on Earth : https://www.pnas.org/doi/10.1073/pnas.1111694108
• We Have No Idea – A Guide to the Unknown Universe : https://www.goodreads.com/book/show/31625636-we-have-no-idea
• Leonardo da Vinci’s biography by Walter Isaacson: https://www.amazon.com/Leonardo-Vinci-Walter-Isaacson/dp/1501139169/ref=sr_1_1?keywords=leonardo+da+vinci+book&qid=1660142880&sprefix=leonardo+%2Caps%2C219&sr=8-1

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I have to confess something: I love challenges. And when you’re a podcaster, what’s a better challenge than dedicating an episode to… visualization? Impossible you say? Well, challenge accepted!

Thankfully, I got the help of a visualization Avenger for this episode — namely, Matthew Kay. Matt is an Assistant Professor jointly appointed in Computer Science and Communications Studies at Northwestern University, where he co-directs the Midwest Uncertainty Collective — I know, it’s a pretty cool name for a lab.

He works in human-computer interaction and information visualization, and especially in uncertainty visualization. He also builds tools to support uncertainty visualization in R. In particular, he’s the author of the tidybayes and ggdist R packages, and wrote the random variable interface in the posterior package.

I promise, you won’t be uncertain about the importance of uncertainty visualization after that…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Matt on Twitter: https://twitter.com/mjskay
• Matt on GitHub: https://github.com/mjskay  
• Matt’s website: https://www.mjskay.com/ 
• Midwest Uncertainty Collective lab: https://mucollective.northwestern.edu/ 
• PyMC find_constrained_priors tutorial: https://www.youtube.com/watch?v=9shZeqKG3M0
• PyMC find_constrained_priors doc: https://www.pymc.io/projects/docs/en/latest/api/generated/pymc.find_constrained_prior.html
• Tutorials / package documentation / videos:
• tidybayes: http://mjskay.github.io/tidybayes/ 
• ggdist: https://mjskay.github.io/ggdist/ (various visualizations in the slabinterval vignette: https://mjskay.github.io/ggdist/articles/slabinterval.html ) 
• Miscellaneous uncertainty visualizations examples: https://github.com/mjskay/uncertainty-examples 
• Talk on uncertainty visualization: https://www.youtube.com/watch?v=E1kSnWvqCw0 
• Biases in probability perception:
• A survey paper on the linear-in-log-odds model of probability perception: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261445/
• Using the linear-in-log-odds model to "debias" uncertainty visualization: https://osf.io/6xcnw/ 

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Folks, there are some new cool kids on the block. They are called PyMC, Aeppl, and Aesara, and it’s high time we give us a proper welcome!

To do that, who better than one of the architects of the new PyMC 4.0 — Ricardo Vieira! In this episode, he’ll walk us through the inner workings of the newly released version of PyMC, telling us why the Aesara backend and the brand new RandomVariable operators constitute such strong foundations for your beloved PyMC models. He will also tell us about a self-contained PPL project called Aeppl, dedicated to converting model graphs to probability functions — pretty cool, right?

Oh, in case you didn’t guess yet, Ricardo is a PyMC developer and data scientist at PyMC Labs. He spent several years teaching himself Statistics and Computer Science at the expense of his official degrees in Psychology and Neuroscience.

So, get ready for efficient random generator functions, better probability evaluation functions, and a fully-fledged modern Bayesian workflow!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Ricardo on Twitter: https://twitter.com/RicardoV944
• Ricardo on GitHub: https://github.com/ricardoV94/
• Ricardo’s website: https://ricardov94.github.io/posts/
• PyMC, Aesara and Aeppl: The New Kids on The Block (YouTube video): https://www.youtube.com/watch?v=_APNiXTfYJw
• Bayesian Vector Autoregression in PyMC: https://www.pymc-labs.io/blog-posts/bayesian-vector-autoregression/
• New PyMC website: https://www.pymc.io/projects/docs/en/stable/learn.html
• Define, optimize, and evaluate mathematical expressions with Aesara: https://aesara.readthedocs.io/en/latest/
• Aeppl documentation: https://aeppl.readthedocs.io/en/latest/
• PyMC’s YouTube channel: https://www.youtube.com/c/PyMCDevelopers
• PyMC on Twitter: https://twitter.com/pymc_devs
• PyMC on LinkedIn: https://www.linkedin.com/company/pymc/mycompany/
• LBS #61, Why we still use non-Bayesian methods, with EJ Wagenmakers: https://www.learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers
• LBS #31, Bayesian Cognitive Modeling & Decision-Making, with Michael Lee:  https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-lee
• We Have No Idea, A Guide to the Unknown Universe: https://www.amazon.com/We-Have-No-Idea-Universe/dp/0735211515

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I’m sure you’ve already heard of gravitational waves, because my listeners are the coolest and smartest ever ;) But did you know about gravity waves? That’s right, waves in the sky due to gravity — sounds awesome, right?

Well, I’m pretty sure that Laura Mansfield will confirm your prior. Currently a postdoc at Stanford University, Laura studies — guess what? — gravity waves and how they are represented in climate models. In particular, she uses Bayesian methods to estimate the uncertainty on the gravity wave components of the models.

Holding a PhD from the University of Reading in the UK, her background is in atmospheric physics, but she’s interested in climate change and environmental issues.

So seat back, chill out, and enjoy this physics-packed, aerial episode!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Laura on Twitter: https://twitter.com/lau_mansfield
• Laura’s webpage: https://profiles.stanford.edu/laura-mansfield
• Julia package for Gaussian Processes: https://github.com/STOR-i/GaussianProcesses.jl 
• Julia implementation of the scikit-learn API: https://github.com/cstjean/ScikitLearn.jl
• Derivative-free Bayesian optimization techniques based on Ensemble Kalman Filters: https://github.com/CliMA/EnsembleKalmanProcesses.jl

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

Inviting someone like Luciano Paz on a stats podcast is both a pleasure and a challenge — he does so many things brilliantly that you have too many questions to ask him…

In this episode, I’ve chosen — not without difficulty — to focus on the applications of Bayesian stats in the marketing industry, especially Media Mix Models. Ok, I also asked Luciano about other topics — but you know me, I like to talk…

Originally, Luciano studied physics. He then did a PhD and postdoc in neuroscience, before transitioning into industry. During his time in academia, he used stats, machine learning and data science concepts here and there, but not in a very organized way.

But at the end of his postdoc, he got into PyMC — and that’s when everything changed… He loved the community and decided to hop on board to exit academia into a better life. After leaving academia, he worked at a company that wanted to do data science but that, for privacy reasons, didn’t have a lot of data. And now, Luciano is one of the folks working full time at the PyMC Labs consultancy.

But Luciano is not only one of the cool nerds building this crazy Bayesian adventures. He also did a lot of piano and ninjutsu. Sooooo, don’t provoke him — either in the streets or at a karaoke bar…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh and Lin Yu Sha.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Luciano’s website: https://lucianopaz.github.io/
• Luciano on GitHub: https://github.com/lucianopaz
• Luciano on LinkedIn: https://www.linkedin.com/in/luciano-paz-4139b5123/
• Bayesian Media Mix Modeling for Marketing Optimization: https://www.pymc-labs.io/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/
• Improving the Speed and Accuracy of Bayesian Media Mix Models: https://www.pymc-labs.io/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/
• Speeding up HelloFresh's Bayesian AB tests by 60x: https://www.pymc-labs.io/blog-posts/bayes-is-slow-speeding-up-hellofreshs-bayesian-ab-tests-by-60x/
• PyMC Labs YouTube channel: https://www.youtube.com/c/PyMCLabs
• LBS #21 Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova: https://www.learnbayesstats.com/episode/21-gaussian-processes-bayesian-neural-nets-sir-models-with-elizaveta-semenova
• Gaussian Processes approximations in PyMC: https://github.com/pymc-devs/pymc-experimental/pull/3
• Michael Betancourt, ​​Identifying Bayesian Mixture Models: https://betanalpha.github.io/assets/case_studies/identifying_mixture_models.html
• Identifying Bayesian Mixture Models in PyMC3: https://gist.github.com/junpenglao/4d65d1a9bf80e8d371446fadda9deb7a
• Mixture Models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-models
• Osvaldo Martin’s Bayesian Analysis with Python: https://www.amazon.com/dp/B07HHBCR9G
• LBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://www.learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson
• Intuitive Bayes Introductory Course: https://www.intuitivebayes.com/

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

We talk a lot about generative modeling on this podcast — at least since episode 6, with Michael Betancourt! And an area where this way of modeling is particularly useful is healthcare, as Maria Skoularidou will tell us in this episode.

Maria is a final year PhD student at the University of Cambridge. Her thesis is focused on probabilistic machine learning and, more precisely, towards using generative modeling in… you guessed it: healthcare!

But her fields of interest are diverse: from theory and methodology of machine intelligence to Bayesian inference; from theoretical computer science to information theory — Maria is knowledgeable in a lot of topics! That’s why I also had to ask her about mixture models, a category of models that she uses frequently.

Prior to her PhD, Maria studied Computer Science and Statistical Science at Athens University of Economics and Business. She’s also invested in several efforts to bring more diversity and accessibility in the data science world.

When she’s not working on all this, you’ll find her playing the ney, trekking or rawing.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton and Jeannine Sue.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Maria on Twitter: https://twitter.com/skoularidou
• Maria on LinkedIn: https://www.linkedin.com/in/maria-skoularidou-1289b62a/
• Maria’s webpage:
• https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/
• Mixture models in PyMC: https://www.pymc.io/projects/examples/en/latest/gallery.html#mixture-models
• LBS #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson: https://learnbayesstats.com/episode/4-dirichlet-processes-and-neurodegenerative-diseases-with-karin-knudson/
• Bayesian mixtures with an unknown number of components: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00095
• Markov Chain sampling methods for Dirichlet Processes: https://www.tandfonline.com/doi/abs/10.1080/10618600.2000.10474879
• Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models: https://academic.oup.com/biomet/article-abstract/95/1/169/219181
• Sampling Dirichlet mixture models with slices: https://www.tandfonline.com/doi/abs/10.1080/03610910601096262
• Label switching problem:
• https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00265
• Mixture Models With a Prior on the Number of Components: https://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1255636
• Approximate Bayesian inference for Gaussian models (R-INLA): https://www.r-inla.org
• Intuitive Bayes Introductory Course: https://www.intuitivebayes.com/
• PyMC Labs corporate workshops: https://www.pymc-labs.io/workshops
• LBS #44 Building Bayesian Models at scale, with Rémi Louf: https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-louf
• Blackjax – Sampling library designed for ease of use, speed and modularity: https://blackjax-devs.github.io/blackjax/