Exploring Bayesian Methods for Missing Data and Discrete Variables

When are Bayesian methods most useful? Conversely, when should you NOT use them? How do you teach them? What are the most important skills to pick-up when learning Bayes? And what are the most difficult topics, the ones you should maybe save for later?

In this episode, you’ll hear Chris Fonnesbeck answer these questions from the perspective of marine biology and sports analytics. Chris is indeed the New York Yankees’ senior quantitative analyst and an associate professor at Vanderbilt University School of Medicine. 

He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He also created PyMC, a library to do probabilistic programming in python, and is the author of several tutorials at PyCon and PyData conferences.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com!

Links from the show:

• Chris on Twitter: https://twitter.com/fonnesbeck
• PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/
• Chris on GitHub: https://github.com/fonnesbeck
• An introduction to Markov Chain Monte Carlo using PyMC3 - PyData London 2019: https://www.youtube.com/watch?v=SS_pqgFziAg
• Introduction to Statistical Modeling with Python - PyCon 2017 - video: https://www.youtube.com/watch?v=TMmSESkhRtI
• Introduction to Statistical Modeling with Python - PyCon 2017 - code repo: https://github.com/fonnesbeck/intro_stat_modeling_2017
• Bayesian Non-parametric Models for Data Science using PyMC3 - PyCon 2018: https://www.youtube.com/watch?v=-sIOMs4MSuA
• Statistical Data Analysis in Python: https://github.com/fonnesbeck/statistical-analysis-python-tutorial