Statistical Rethinking is an excellent book for applied Bayesian data analysis. The accompanying codes for the book are written in R and Stan. They are then ported to Python language using PyMC3. Recently, Pyro emerges as a scalable and flexible Bayesian modeling tool (see its tutorial page), so to attract statisticians to this new library, I decided to make a Pyronic version for the codes in this repository. Inspired by the PyMC3onic version, I keep the codes in this repository as close as possible to the original codes in the book.
To say a bit more about Pyro, it is a universal probabilistic programming language which is built on top of PyTorch, a very popular platform for deep learning. If you are familiar with numpy, the transition from
torch.tensor is rather straightforward (as demonstrated in this tutorial).
pip install jupyter pandas pyro-ppl seaborn torch