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

#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!

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