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.

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.

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

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.

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 text is excellent for a beginners introduction to Bayesian statistics. I was using JASP for my analyses rather than the authors packages, but it helped a lot with my general understanding of what happening behind the scenes. I look forward to using his R package for my next data project.

Muy buen producto y llego a tiempo

ベイズ統計に興味があって購入しました。内容は丁寧に書かれており、初心者にもわかりやすい構成です。数式も多すぎず、実例を交えながら進むので、実践的な理解が深まります。理論だけでなく、実装についても触れられているのがありがたいです。ベイズに入門したい方にはおすすめの一冊です。

I am a PhD in berlin I have been literally blown away by this book. It does not only teach you statistics. It makes you a better scientist.

Lots of positives about this book: - Accompanying lectures by the author which are available online for free on his YouTube channel - Author tries to make Bayesian stats as intuitive as possible, and most explanations are by examples and code rather than written math. - Places heavy emphasis on the use of Bayesian stats for inference rather than predictive modelling (but does explain the importance of good model fit, etc. as well). - Explains how to set good priors, with examples, which is usually missing in a lot of other instructive material on Bayesian modelling. Some things to note that might be issues depending on your specific needs: - Examples are pretty reliant on the rethinking package, instead of pure Stan or rstan. This is a small issue though since there are reference manuals online for how to use those tools (the book is more about teaching the Bayesian way of thinking and causal inference rather than a specific tool). - There is a focus on the social sciences so there's little application to 'bigger data' domains where distributions are a little different and data size can be an issue for Bayesian inference (e.g. Tech). Book will provide good fundamentals for extending to this kind of domain though. - Probably not for more intermediate or advanced users of Bayesian stats (e.g. you've already built a few models end to end).

Probably the best book you can read as a newbie in Bayesian modeling and statistical modeling in general. Covers the theory, not getting into much depth, but also the philosophy of science and modeling and the practical implications of each technique. Well written and with many interesting references to foundational writings in each topic.