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- 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:
- 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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