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Relatedly, I think for introductory Bayesian analysis, Statistical Rethinking is the best book. It uses R code but you will find GitHub repositories for translated Python code.

In the R ecosystem, brms is currently the best (in my opinion) package for people who want to learn Bayesian analysis. Here is an online free book that translated all the original R code to brms code: https://bookdown.org/content/4857/

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I prefer Gelman, Hill & Vehtari Regression and Other Stories (ROS). It's also free: https://avehtari.github.io/ROS-Examples.

It's a great prequel to Gelman et al. Bayesian Data Analysis (BDA). I have a formal background in Bayesian inference, I do statistics for a living and I still learned many things from ROS. Yet, it is totally approachable for beginners.

Statistical Rethinking is something you can read after ROS, before BDA. Personally, I think it is a great book, but it's not a good first book and I think it would benefit from some editing. For example, chapter titles are funny, but totally uninformative if you are browsing the book. Nonetheless, I still think it's a great read.

I still refer to the R notebooks I created in doing the question sets for RaOS. A very high quality book.

Oh yes, it is an excellent book too.

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