Advanced Bayesian Regression, with Tomi Capretto

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!

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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, Omr