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