--- product_id: 9032695 title: "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan" price: "€ 135.05" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.at/products/9032695-doing-bayesian-data-analysis-a-tutorial-with-r-jags-stan store_origin: AT region: Austria --- # Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan **Price:** € 135.05 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan - **How much does it cost?** € 135.05 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.at](https://www.desertcart.at/products/9032695-doing-bayesian-data-analysis-a-tutorial-with-r-jags-stan) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step by step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric predicted variable on one or two groups; metric predicted variable with one metric predictor; metric predicted variable with multiple metric predictors; metric predicted variable with one nominal predictor; and metric predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and JAGS software Comprehensive coverage of all scenarios addressed by non Bayesian textbooks: t tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi square (contingency table analysis) Coverage of experiment planning R and JAGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment Provides step by step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. Review: Excellent Resource - This book is outstanding. The author covers Bayesian analysis starting with the assumption that you know virtually nothing about it and builds to the point that you can do actual, meaningful analysis, interpret the results and communicate them to people that are not aware of Bayesian techniques. (I bought the book because of the "See inside" feature. Page 5 sealed the deal. If you do any type of statistical analysis check it out.) The writing is clear and there are numerous examples that are typically interesting which really helps. The author has a good sense of humor as well which is rare in a book that covers advanced material like this. The book is long. (>700 pages) But there is a LOT of material being covered. The "Doing" part of the book is done with R, JAGS and Stan, so if you aren't familiar with any of those, it's a lot to take in. I wasn't familiar with any of thee and I did fine. I had to read some parts multiple times but that might just be me. I did most of the exercises which really helped. (notable exception: 13.1) The book seems expensive at first. It's a textbook so there's that. However, you also get a very large number of R scripts to demonstrate the concepts. The scripts are useful and in my opinion worth as much as the book. (All of the software is free.) I have already used the scripts to suit analysis I needed to do. The book also really covers multiple topics so once I got into it I realized I got a great deal. I have received more value that I paid. Don't buy this thinking you are going to breeze through it and be able to actually *do* Bayesian analysis well. The book is true to the title but only if you put forth the time and effort. You absolutely can learn an enormous amount from this book. To the author, if you're reading this: Thank you! I am better at what I do because of this book. Review: Very Good - Edit: I've updated the rating from 1-star to 5 stars to properly reflect the quality of the content. Anything else would be unfair. I returned my original copy which was in an unacceptable condition (see text below plus the comments). I didn't mind the hazzle much, but I did incur some additional costs. I had to send the original copy back to desertcart which cost more than the 15$ refund, and I also had to pay for the (quick) delivery cost. Nonetheless, my impression is that the book is possibly the best introduction to Bayesian statistics on the market. And not only the best, but also very good in its own right. The book by Gelman et. al. is a leading textbook on the subject, and for a good reason, but the authors assume their readers have mastered intermediary statistics and have received a thorough prior introduction to Bayesian statistics. John Kruschke, in contrast, assumes very little knowledge of the former, and none of the latter. All expositions are intuitive rather than technical. The chapter on R taught me that I still have much to learn on the language, and makes me wonder how inefficient and cumbersome my R code has been thus far. I'm trying to think of any real complaints I have that are not merely a reflection of my eccentric nature, but I keep coming up short. Recommended. ------------- Please be aware that my review concerns the quality of the binding of the pages in the book, and not the actual intellectual content. I've only recently begun reading it, and I have no reason to believe that the other positive reviews are anything other than accurate. But yes, good as the exposition of the subject must be, the binding of the pages is poor in regular patterns. Although no pages have fallen out, I can't help but wonder if the book will stay in one piece for very long. I don't know if you can see the picture that I uploaded, but it shows only one opening, on page 82-83. Other openings are either similar or share the same fate. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #227,542 in Books ( See Top 100 in Books ) #52 in Statistics (Books) #53 in Mathematical Analysis (Books) #197 in Probability & Statistics (Books) | | Customer Reviews | 4.6 out of 5 stars 214 Reviews | ## Images ![Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan - Image 1](https://m.media-amazon.com/images/I/51A4QA6VPsL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Excellent Resource *by J***N on October 30, 2015* This book is outstanding. The author covers Bayesian analysis starting with the assumption that you know virtually nothing about it and builds to the point that you can do actual, meaningful analysis, interpret the results and communicate them to people that are not aware of Bayesian techniques. (I bought the book because of the "See inside" feature. Page 5 sealed the deal. If you do any type of statistical analysis check it out.) The writing is clear and there are numerous examples that are typically interesting which really helps. The author has a good sense of humor as well which is rare in a book that covers advanced material like this. The book is long. (>700 pages) But there is a LOT of material being covered. The "Doing" part of the book is done with R, JAGS and Stan, so if you aren't familiar with any of those, it's a lot to take in. I wasn't familiar with any of thee and I did fine. I had to read some parts multiple times but that might just be me. I did most of the exercises which really helped. (notable exception: 13.1) The book seems expensive at first. It's a textbook so there's that. However, you also get a very large number of R scripts to demonstrate the concepts. The scripts are useful and in my opinion worth as much as the book. (All of the software is free.) I have already used the scripts to suit analysis I needed to do. The book also really covers multiple topics so once I got into it I realized I got a great deal. I have received more value that I paid. Don't buy this thinking you are going to breeze through it and be able to actually *do* Bayesian analysis well. The book is true to the title but only if you put forth the time and effort. You absolutely can learn an enormous amount from this book. To the author, if you're reading this: Thank you! I am better at what I do because of this book. ### ⭐⭐⭐⭐⭐ Very Good *by S***N on March 23, 2015* Edit: I've updated the rating from 1-star to 5 stars to properly reflect the quality of the content. Anything else would be unfair. I returned my original copy which was in an unacceptable condition (see text below plus the comments). I didn't mind the hazzle much, but I did incur some additional costs. I had to send the original copy back to Amazon which cost more than the 15$ refund, and I also had to pay for the (quick) delivery cost. Nonetheless, my impression is that the book is possibly the best introduction to Bayesian statistics on the market. And not only the best, but also very good in its own right. The book by Gelman et. al. is a leading textbook on the subject, and for a good reason, but the authors assume their readers have mastered intermediary statistics and have received a thorough prior introduction to Bayesian statistics. John Kruschke, in contrast, assumes very little knowledge of the former, and none of the latter. All expositions are intuitive rather than technical. The chapter on R taught me that I still have much to learn on the language, and makes me wonder how inefficient and cumbersome my R code has been thus far. I'm trying to think of any real complaints I have that are not merely a reflection of my eccentric nature, but I keep coming up short. Recommended. ------------- Please be aware that my review concerns the quality of the binding of the pages in the book, and not the actual intellectual content. I've only recently begun reading it, and I have no reason to believe that the other positive reviews are anything other than accurate. But yes, good as the exposition of the subject must be, the binding of the pages is poor in regular patterns. Although no pages have fallen out, I can't help but wonder if the book will stay in one piece for very long. I don't know if you can see the picture that I uploaded, but it shows only one opening, on page 82-83. Other openings are either similar or share the same fate. ### ⭐⭐⭐⭐⭐ Simply the Best *by R***H on January 17, 2015* Over the past couple years, I've been trying to learn Bayesian statistics, both for theoretical understanding and for practical use in my job. That has lead me to read 5-10 different books on the subject (with a range of scopes and focuses), which lead me to read the first edition of this book. Some of the books that I read are better than others, but I can easily say that Kruschke's was the best introductory book I found. That is NOT to say that it lacks rigor. What it does is start off with the basics, and it communicates in a clear, readable, and often humorous approach. What it does not do is assume that you have an advanced degree in statistics. In addition, the book gives you TONS of useful programming help in R, all in downloadable files. Even better (for me at least) were the programs that helped access OpenBUGS from R (in the first edition). That is a tricky process, and I found the book's insight and programs to be very valuable. I would also like to thank the author for reading my mind. After having worked with OpenBUGS for a little while, I was hearing good things about STAN, and I've been wanting to give it a try. Right about the time I decided that, this second edition came out, and this time it includes STAN. Woot! I'm reading through the second edition, and I'm enjoying it just as much as the first. Heck, this book is probably worth the price for the programs alone. ## Frequently Bought Together - Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan - 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) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.at/products/9032695-doing-bayesian-data-analysis-a-tutorial-with-r-jags-stan](https://www.desertcart.at/products/9032695-doing-bayesian-data-analysis-a-tutorial-with-r-jags-stan) --- *Product available on Desertcart Austria* *Store origin: AT* *Last updated: 2026-05-23*