Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)

Over the years I've bought many Bayesian Analysis textbooks, the reason being I knew from ML academics that working with distributions is the "true" way of doing ML instead of just point estimates like in industrial ML. Before picking up this book I had given up on ever finding a real use-case for Bayesian ML because most of the other often recommended textbooks I owned were happy with long tedious mathematical derivations that wouldn't even bother explaining why a technique is important or how to implement it. This book is exceptional in that it gives you the historical context behind how certain techniques were evolved and an excellent intuition for how they work. The book also comes with an R Bayesian Analysis library which also has excellent ports in Julia and Python on Github. In the case where you don't have gigantic amounts of data and where you'd like to question assumptions that you have about data, this book will teach you a way of how to think about data that is sorely lacking in any sort of Deep Learning text. All of the algorithms in this book have stood the test of time and will continue to be relevant for the foreseeable future, thankfully this book exists to make these algorithms understandable.

I must confess that I was quite hesitant to pick this book up when I first encountered the strong recommendations of experienced Bayesian practitioners. The 'cutesy' chapter titles and topics really threw me off, and as someone who actively uses Bayesian stats I was sure there would not be much for me to learn from this book. Well, I was quite wrong. This is one of the most enjoyable technical books I have read in a long time and it really helped focus my skills and put the tools of Bayesian stats in perspective. Highly recommended to even experienced data scientists... even when he is covering ground you know well, it gives you a new way to think and communicate it to others.

Very good, accessible, and worth it. While a background in frequentist isn't required, it is suggested. This book definitely allows you to both learn and apply Bayesian analysis as you would in the "real world," being more applied than theoretical. Excellent if you're a scientist or statistician wanting to finally break Bayesian, or just buff up on some R skills.

This book was recommended from a colleague who is a former AWS Cloud Engineer, and another who is a fantastic Statistician. Both have said this is the book to read if you want to understand Bayesian statistics, but does not cover how and why it is superior to frequentist. This is not a basic statistics book and does not cover p-values. Recommend for MS or PhD students with a strong math background

I’ve been a professional statistician for a long time, and I’ve read or tried to read a ton of books. This book covers a a lot of the tools used in day to day practice, provides clearly written useful advice, and has a practical point of view that is both mathematically sound and helps build the reader’s data intuition. One of the best statistics books I’ve ever read.

Clear description, but none of the hyperlinks work. Everything in blue doesn’t link correctly.

I have read and used BDA3 by Gelman et al. and thought I would not read another Bayesian analysis book. But this book is like a romantic Bayesian novel -- reading every page makes me want to read the next... It's an awesome book and I recommend it to anyone interested in the beautiful Bayes' world!

This book has a good balance between examples, theory, practical application. Even though it includes R code, the GitHub site includes conversion to Python and other programming languages