I’m a big fan of Bayesian data analysis using probabilistic programming. Recently, I took a course about Bayesian Data Analysis using the book Doing Bayesian Data Analysis 2nd edition by John K. Kruschke. It’s a great book, and it covers many topics about data analysis using bayesian approach. I highly recommend the book to anyone who wants to learn about bayesian data analysis. The implementations in the book are in R and JAGS/Stan. Thus, I think this will be a good practice to port the models described in the book to Python/Numpyro.

The choice between PyMC3 and Numpyro for this port boils down to speed. My previous experiences with PyMC3 have been a let down since PyMC3 was slow for what I did. Numpyro, on the other hand, is blazing fast! In addition, there is already a great port of the models in the book to Python/PyMC3.

Without further ado, here are the chapters:

• Chapter 9: Hierarchical Models
• Chapter 10: Model Comparison and Hierarchical Modeling
• Chapter 12: Bayesian Approaches to Testing a Point (“Null”) Hypothesis
• Chapter 16: Metric-Predicted Variable on One or Two Groups
• Chapter 17: Metric Predicted Variable with one Metric Predictor
• Chapter 18: Metric Predicted Variable with Multiple Metric Predictors
• Chapter 19: Metric Predicted Variable with One Nominal Predictor
• Chapter 20: Metric Predicted Variable with Multiple Nominal Predictors
• Chapter 21: Dichotomous Predicted Variable
• Chapter 22: Nominal Predicted Variable
• Chapter 23: Ordinal Predicted Variable
• Chapter 24: Count Predicted Variable

If you want to take a look at the source code, here is the repository. As I’m learning about bayesian data analysis and Numpyro, there will be bugs and unoptimized ways of doing things in the code. Any suggestions for improvement and bug fixes are welcome!