- Hardcover: 776 pages
- Publisher: Academic Press; 2 edition (November 17, 2014)
- Language: English
- ISBN-10: 0124058884
- ISBN-13: 978-0124058880
- Product Dimensions: 7.5 x 1.5 x 9.2 inches
- Shipping Weight: 3.8 pounds (View shipping rates and policies)
- Average Customer Review: 63 customer reviews
- Amazon Best Sellers Rank: #38,424 in Books (See Top 100 in Books)
Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan 2nd Edition
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"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.
Top customer reviews
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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.
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.
Don't dive too deep into the mathematics but into the application and I liked that.
What I liked about
1-Gives examples that are relatable
2-Has videos online to support the content that he had produced.
3-Explained the concepts in a clear and relatable manner
All of the explanations are intuitive and go step-by-step, without assuming much prior knowledge.
The programs are great and explained clearly. They are easily modified (I have done so successfully for my class).
I also really like the author's style of plots which are produced by his programs. They are clear, easily interpreted, and visually appealing.