#112 Advanced Bayesian Regression, with Tomi Capretto
Learning Bayesian Statistics
Engaging Bayesian Learning with M&Ms
This chapter discusses an innovative classroom exercise using M&Ms to teach students about Bayesian statistics and patient base through hands-on exploration of color proportions. The narrative highlights the transition students face from classical to Bayesian methods, emphasizing the benefits of interactive learning and Monte Carlo techniques. Additionally, it introduces Bambi, a Python tool for regression modeling that parallels R’s capabilities, showcasing the evolution of statistical methods across different programming languages.
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Takeaways:
- Teaching Bayesian Concepts Using M&Ms: Tomi Capretto uses an engaging classroom exercise involving M&Ms to teach Bayesian statistics, making abstract concepts tangible and intuitive for students.
- Practical Applications of Bayesian Methods: Discussion on the real-world application of Bayesian methods in projects at PyMC Labs and in university settings, emphasizing the practical impact and accessibility of Bayesian statistics.
- Contributions to Open-Source Software: Tomi’s involvement in developing Bambi and other open-source tools demonstrates the importance of community contributions to advancing statistical software.
- Challenges in Statistical Education: Tomi talks about the challenges and rewards of teaching complex statistical concepts to students who are accustomed to frequentist approaches, highlighting the shift to thinking probabilistically in Bayesian frameworks.
- Future of Bayesian Tools: The discussion also touches on the future enhancements for Bambi and PyMC, aiming to make these tools more robust and user-friendly for a wider audience, including those who are not professional statisticians.
Chapters:
05:36 Tomi's Work and Teaching
10:28 Teaching Complex Statistical Concepts with Practical Exercises
23:17 Making Bayesian Modeling Accessible in Python
38:46 Advanced Regression with Bambi
41:14 The Power of Linear Regression
42:45 Exploring Advanced Regression Techniques
44:11 Regression Models and Dot Products
45:37 Advanced Concepts in Regression
46:36 Diagnosing and Handling Overdispersion
47:35 Parameter Identifiability and Overparameterization
50:29 Visualizations and Course Highlights
51:30 Exploring Niche and Advanced Concepts
56:56 The Power of Zero-Sum Normal
59:59 The Value of Exercises and Community
01:01:56 Optimizing Computation with Sparse Matrices
01:13:37 Avoiding MCMC and Exploring Alternatives
01:18:27 Making Connections Between Different Models
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:
- Tomi’s website: https://tomicapretto.com/
- Tomi on GitHub: https://github.com/tomicapretto
- Tomi on Linkedin: https://www.linkedin.com/in/tom%C3%A1s-capretto-a89873106/
- Tomi on Twitter: https://x.com/caprettotomas
- Advanced Regression course (get 10% off if you’re a Patron of the show): https://www.intuitivebayes.com/advanced-regression
- Bambi: https://bambinos.github.io/bambi/
- LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
- LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/
- patsy - Describing statistical models in Python: https://patsy.readthedocs.io/en/latest/
- formulae - Formulas for mixed-models in Python: https://bambinos.github.io/formulae/
- Introducing Bayesian Analysis With m&m's®: An Active-Learning Exercise for Undergraduates: https://www.tandfonline.com/doi/full/10.1080/10691898.2019.1604106
- Richly Parameterized Linear Models Additive, Time Series, and Spatial Models Using Random Effects https://www.routledge.com/Richly-Parameterized-Linear-Models-Additive-Time-Series-and-Spatial-Models-Using-Random-Effects/Hodges/p/book/9780367533731
- Dan Simpson’s Blog (link to blogs with the ‘sparse matrices’ tag): https://dansblog.netlify.app/#category=Sparse%20matrices
- Repository for Sparse Matrix-Vector dot product: https://github.com/tomicapretto/dot_tests
Transcript
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