Going through the book Statistical Rethinking (2nd edition) by Richard McElreath in an attempt to learn Bayesian modeling starting from zero. I'm a python kind of guy, so I think I'm going to try and redo all the code examples using one of the various PPL's (Probabilistic Programming Languages) that exist in the python universe. I have been getting more into pytorch lately as a framework for autodifferentiation and neural networks, and there is a nice-looking package called pyro for Bayesian inference that is built on top of it, so I will try and use that.

I think this is a much better idea than learning R so that I can copy McElreath's code, because I have learned so much more by implementing things from scratch rather than relying on his custom-built quap, precis, and other functions as black boxes that simply give you the answer and hide away a lot of the implementation details.

Du Phan, one of the maintainers of the package, is doing something similar, so their repo can serve as a comparison.

I will also use a mixture of numpy, sklearn, pandas, matplotlib, etc. for various other things if the need arises rather than go straight to torch/pyro (especially for simpler problems).

The data used can be found in the official repository for the book. Noticed some files were missing (like cars.csv), but they can be found here.