#123 BART & The Future of Bayesian Tools, with Osvaldo Martin

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Jan 10, 2025

Osvaldo Martin, a collaborator on various open-source Bayesian projects and educator at PyMC Labs, discusses the power of Bayesian Additive Regression Trees (BART). He explains how BART simplifies modeling for those lacking domain expertise. The conversation also highlights advancements in tools like PyMC-BART and PreliZ, emphasizing their contributions to prior elicitation. Osvaldo shares insights on integrating BART with Bambi and the importance of interactive learning in teaching Bayesian statistics. Additionally, he touches upon future enhancements for user experience in Bayesian analysis.

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INSIGHT

BART Models

BART models are non-parametric Bayesian models that approximate functions by summing trees.
They are suitable for quick modeling without extensive domain knowledge.

ADVICE

When to Use BART

Use BART models when you need quick results or lack domain expertise for tailored models.
They are also effective when you are primarily interested in variable importance analysis.

INSIGHT

Variable Importance in PyMC-BART

PyMC-BART offers a unique method for interpreting variable importance.
It compares predictions from submodels to the full model, making it easier to identify key variables.

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Takeaways:

BART models are non-parametric Bayesian models that approximate functions by summing trees.
BART is recommended for quick modeling without extensive domain knowledge.
PyMC-BART allows mixing BART models with various likelihoods and other models.
Variable importance can be easily interpreted using BART models.
PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
The integration of BART with Bambi could enhance exploratory modeling.
Teaching Bayesian statistics involves practical problem-solving approaches.
Future developments in PyMC-BART include significant speed improvements.
Prior predictive distributions can aid in understanding model behavior.
Interactive learning tools can enhance understanding of statistical concepts.
Integrating PreliZ with PyMC improves workflow transparency.
Arviz 1.0 is being completely rewritten for better usability.
Prior elicitation is crucial in Bayesian modeling.
Point intervals and forest plots are effective for visualizing complex data.

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

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, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric.

Links from the show:

LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/
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/
LBS #112 Advanced Bayesian Regression, with Tomi Capretto: https://learnbayesstats.com/episode/112-advanced-bayesian-regression-tomi-capretto/
Osvaldo's website: https://aloctavodia.github.io/
Osvaldo on GitHub: https://github.com/aloctavodia
Osvaldo on LinkedIn: https://www.linkedin.com/in/osvaldo-martin-447a662b1/
Osvaldo on Google Scholar: https://scholar.google.com/citations?user=WUvDNnkAAAAJ
Osvaldo on Mastodon: https://bayes.club/@aloctavodia
Osvaldo on BlueSky: https://bsky.app/profile/aloctavodia.bsky.social
PyMC-BART package: https://www.pymc.io/projects/bart/en/latest/index.html
PyMC-BART paper: https://arxiv.org/abs/2206.03619
PreliZ for prior elicitation: https://preliz.readthedocs.io/en/latest/
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
ArviZ 1.0 repository: https://arviz-plots.readthedocs.io/en/latest/
Practical MCMC course: https://www.intuitivebayes.com/practical-mcmc
Cohort Retention Analysis with BART: https://juanitorduz.github.io/retention_bart/
HSGP Reference & First Steps: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Basic.html
HSGP Advanced Usage: https://www.pymc.io/projects/examples/en/latest/gaussian_processes/HSGP-Advanced.html

Transcript

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