Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 2nd Edition by John Kruschke (PDF)

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 samplingExamples with R programming language and JAGS softwareComprehensive 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 planningR and JAGS computer programming code on websiteExercises have explicit purposes and guidelines for accomplishmentProvides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐It’s typical to only review something you don’t like, but I found this book so very helpful that I found it difficult to not write a review. This book does a good job of demystifying and thereby removing the fear of Bayesian statistics. As I pace my way through the book, I find myself feeling as though I really can grok this information and put it to good use. This book provides a sensible measure of the theoretical and practical, making mathematical minds out of the not-so-mathematically minded. I feel confident that I’ll walk away a practitioner who will understand precisely what it is he is practicing.

⭐I’m college-level math capable. From the very start, I was confused and perplexed by the author’s coherence, which ranged from poetry (yes, poetry) to truncated explanations (see photo of Figure 2.6). In this example (of height predicting weight) the author carefully explains that the actual weight, y, is distributed randomly according to a normal distribution around the predicted value of y with a standard deviation (p. 27).Then he shows us Figure 2.6, with the posterior predicted weight values superimposed at height values. Each vertical bar shows a 95% range of the most credible predicted weight values.I could not figure out where he got this 95% range from. It’s not from the standard deviation of the weight values, as far as I could tell. Exactly where did that sigma come from?It was like this through all of the first part of this book.I am led to conclude that while the author was talking to undergraduates, he did not take into account imprecision in his presentation (which perhaps he could account for in a lecture format?).I wonder if Academic Press will put out a 3rd edition. If so, I hope they correct the confusing parts.Finally, I really get frustrated by undergraduate math texts that provide no solutions for exercises. Would not the answers (say, provided at the back) be useful in interpreting the text?

⭐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.

⭐As others have mentioned, it’s a solid introductory book for those who learn best from examples rather than theory. I don’t think you leave this book being a Bayesian expert, but you can start applying Bayesian techniques in your work and then look to other books and research to further refine your knowledge. It’s very good for getting an overview. There were a few places where I wished he had gone into more detail – I was using PyMC3 instead of JAGS or Stan to follow along, and it took some effort to reconstruct everything. (I would recommend to others that you try and go a little deeper than just using the supplied scripts, I think you’ll learn more)I bought both the hardcover and Kindle versions of the book. The author did not control the production of the Kindle version (he’s stated that pretty clearly on his website), and unfortunately it has a lot of mistakes. E.g. even the derivation of Bayes Theorem has math errors. The capital letter “R” is replaced throughout the entire book with a stylised image of an italicised R. Clearly they meant to target the statistical tool, “R”, but they also managed to impact any place where a capital R might show up otherwise (e.g. the phrase “Read Chapter 12 for more details”). It was sloppy.

⭐Clear and informative book. It wasn’t lacking in explanation(s) and should be use-able by A level maths students and above. I like the book and as yet haven’t finished working through the book in its entirety but what I have looked at (75%) has exceeded my expectations. A worthwhile subjective book that gives the core concepts.

⭐Best book on Bayesian analyses I know.

⭐I think this is a perfect book for somebody who Is a beginner in terms of Bayesian data analysis. I still recommend some knowledge of probability or stats to fully understand the subject matter, but the book does not absolutely require it.

⭐A great book. The author has succeeded in writing a book that is both informative and inspirational. For this I am grateful and happy. I am on a quest for learning more on Bayesian statistics. And as I am a working statistician, I hope that I can put the things I learn in good use.

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