---
product_id: 181027572
title: "Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)"
price: "€ 91.96"
currency: EUR
in_stock: true
reviews_count: 13
url: https://www.desertcart.ie/products/181027572-statistical-rethinking-a-bayesian-course-with-examples-in-r-stan
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region: Ireland
---

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

**Price:** € 91.96
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- **What is this?** Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)
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## Description

desertcart.com: Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science): 9780367139919: McElreath, Richard: Books

Review: The Only readable Bayesian Analysis book I own - 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.
Review: Excellent course in Bayesian statistics - 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.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #105,509 in Books ( See Top 100 in Books ) #79 in Probability & Statistics (Books) #90 in Sociology Research & Measurement #220 in Computer Software (Books) |
| Customer Reviews | 4.8 4.8 out of 5 stars (374) |
| Dimensions  | 7.09 x 1.38 x 10.24 inches |
| Edition  | 2nd |
| ISBN-10  | 036713991X |
| ISBN-13  | 978-0367139919 |
| Item Weight  | 3.15 pounds |
| Language  | English |
| Part of series  | Chapman & Hall/CRC Texts in Statistical Science |
| Print length  | 594 pages |
| Publication date  | March 16, 2020 |
| Publisher  | Chapman and Hall/CRC |

## Images

![Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science) - Image 1](https://m.media-amazon.com/images/I/81cEpsiTCFL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ The Only readable Bayesian Analysis book I own
*by M***M on June 15, 2020*

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.

### ⭐⭐⭐⭐⭐ Excellent course in Bayesian statistics
*by D***Y on March 20, 2022*

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.

### ⭐⭐⭐⭐⭐ One of the best statistics books ever written
*by T***M on August 9, 2022*

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.

## Frequently Bought Together

- Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)
- Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

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*Last updated: 2026-03-11*