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Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan, by John Kruschke
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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
- Sales Rank: #107646 in Books
- Published on: 2014-11-17
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.70" w x 7.80" l, 3.70 pounds
- Binding: Hardcover
- 776 pages
Review
"Both textbook and practical guide, this work is an accessible account of Bayesian data analysis starting from the basics…This edition is truly an expanded work and includes all new programs in JAGS and Stan designed to be easier to use than the scripts of the first edition, including when running the programs on your own data sets." --MAA Reviews, Doing Bayesian Data Analysis, Second Edition
“fills a gaping hole in what is currently available, and will serve to create its own market” Prof. Michael Lee, U. of Cal., Irvine; pres. Society for Mathematical Psych. “has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" Prof. Geoffrey Iverson, U. of Cal., Irvine; past pres. Society for Mathematical Psych. “better than others for reasons stylistic.... buy it -- it’s truly amazin’!” James L. (Jay) McClelland, Lucie Stern Prof. & Chair, Dept. of Psych., Stanford U. "the best introductory textbook on Bayesian MCMC techniques" J. of Mathematical Psych. "potential to change the methodological toolbox of a new generation of social scientists" J. of Economic Psych. "revolutionary" British J. of Mathematical and Statistical Psych. "writing for real people with real data. From the very first chapter, the engaging writing style will get readers excited about this topic" PsycCritiques
From the Back Cover
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.
About the Author
John K. Kruschke is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics, at Indiana University in Bloomington, Indiana, USA. He is eight-time winner of Teaching Excellence Recognition Awards from Indiana University. He won the Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of various scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, and the Journal of Mathematical Psychology, among others.
After attending the Summer Science Program as a high school student and considering a career in astronomy, Kruschke earned a bachelor's degree in mathematics (with high distinction in general scholarship) from the University of California at Berkeley. As an undergraduate, Kruschke taught self-designed tutoring sessions for many math courses at the Student Learning Center. During graduate school he attended the 1988 Connectionist Models Summer School, and earned a doctorate in psychology also from U.C. Berkeley. He joined the faculty of Indiana University in 1989. Professor Kruschke's publications can be found at his Google Scholar page. His current research interests focus on moral psychology.
Professor Kruschke taught traditional statistical methods for many years until reaching a point, circa 2003, when he could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way, and after only several thousand hours of relentless effort, the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.
Most helpful customer reviews
29 of 29 people found the following review helpful.
Simply the Best
By Troy McClure
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.
9 of 9 people found the following review helpful.
Excellent Resource
By Joseph C. Eaton
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.
11 of 11 people found the following review helpful.
with just enough math to be believable but not too much to be painful. What really stands out here are the high-level ...
By br00t
Fantastically written. Entertaining and informative, with just enough math to be believable but not too much to be painful. What really stands out here are the high-level explanations of concepts. The way the author describes the Metropolis algorithm, Gibbs sampling and a a number of other ideas critical to having a good grasp of Bayesian methods really highlight the almost magical coolness of Bayesian inference. Excellent book!
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