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Doing Bayesian Data Analysis: A Tutorial with R and BUGS Hardcover – November 10, 2010

ISBN-13: 978-0123814852 ISBN-10: 0123814855 Edition: 1st

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Product Details

  • Hardcover: 672 pages
  • Publisher: Academic Press; 1 edition (November 10, 2010)
  • Language: English
  • ISBN-10: 0123814855
  • ISBN-13: 978-0123814852
  • Product Dimensions: 9.3 x 7.6 x 1.3 inches
  • Shipping Weight: 2.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (52 customer reviews)
  • Amazon Best Sellers Rank: #145,563 in Books (See Top 100 in Books)

Editorial Reviews


"This book is head-and-shoulders better than the others I've seen.  I'm using it myself right now.  Here's what's good about it: .It builds from very simple foundations. .Math is minimized.  No proofs. .From start to finish, everything is demonstrated through R programs. .It helps you learn Empirical Bayesian methods from every angle."--Exploring Possibility Space blog, March 12, 2014

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.

Customer Reviews

I highly recommend this book even if you are not particularly interested in Bayesian frame work.
I consider John Kruschke's "Doing Bayesian Data Analysis" to be the best text available for learning this branch of statistics.
Joseph Hilbe
It's well written, easy to understand and provides many practical and thoroughly explained examples.
Andrew Hatch

Most Helpful Customer Reviews

113 of 114 people found the following review helpful By Joseph Hilbe on May 12, 2011
Format: Hardcover
I have reviewed a number of statistics texts for academic journals over the years, and have authored published reviews of some six books specifically devoted to Bayesian analysis. I consider John Kruschke's "Doing Bayesian Data Analysis" to be the best text available for learning this branch of statistics.

Learning how to craft meaningful statistical tests and models based on Bayesian methods is not an easy task. Nor is it an easy task to write a comprehensive basic text on the subject -- one that actually guides the reader through the various Bayesian concepts and mathematical operations so that they have a solid working ability to develop their own Bayesian-based analyses.

There are now quite a few texts to choose from in this area, and some are quite good. But Kruschke's text, in my opinion, is the most useful one available. It is very well written, the concepts unique to the Bayesian approach are clearly presented, and there is an excellent instructors manual for professors who have adopted the book for their classes. Kruschke uses R and WinBUGS for showing examples of the methods he describes, and provides all of the code so that the reader can adapt the methods for their own projects.

"Doing Bayesian Data Analysis" is not just an excellent text for the classroom, but also -- and I think foremost -- it is just the text one would want to work through in order to learn how to employ Bayesian methods for oneself.

Joseph Hilbe
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61 of 62 people found the following review helpful By Sitting in Seattle on May 6, 2011
Format: Hardcover Verified Purchase
I highly recommend this book to two audiences: (a) instructors looking to construct a strong course on "introduction to social science statistics" from a Bayesian perspective; and (b) social science researchers who have been educated in a classical framework and wish to learn the foundational knowledge of a Bayesian approach, without a refresher in differential calculus. (I expect it would also of interest to many physical science and engineering researchers whose methods are not highly divergent from social science (e.g., biologists, operations engineers) but I can't speak authoritatively about that.)

I'm a practicing social science researcher and have wanted for years to learn Bayesian methods deeply - I've used them in applied settings but without complete understanding. My quest to learn Bayesian methods more rigorously has been persistently stymied by texts that demand analytic solutions to prior/posterior estimation, that are excruciatingly focused on specific problems with little attention to generalization, or that skip huge areas of exposition to leap from a toy problem to a complex one with little clue of the path between them. Dr. Kruschke's text avoids all of those problems. It is remarkable for building intuition from basic principles, for avoiding page-after-page of integrals, and for having extremely clear application.

The book starts by laying out the core intuitions of Bayes's rule - instead of merely stating it (and don't we all think we know it by now?), it leads the reader through some applied examples with frequency tables. Simple? Yes; but also valuable to force oneself through.
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39 of 40 people found the following review helpful By Dimitri Shvorob on December 7, 2011
Format: Hardcover
As far as I am concerned, if you write a book this good, you get to put whatever you like on the cover - puppies, Angelina Jolie, even members of the metal band "Das Kruschke". While reading "DBDA" - reading *and* stepping through the code examples - will not make you a "Bayesian black-belt", it's impressive how much information it *will* give you - the book is almost 700 pages, after all - and you don't need (but it helps) to have tried to get the hang of the "Bayesian stuff" with other books to appreciate how friendly and effective this one is. (The author's explanation of the Metropolis algorithm is a good example). At the risk of sounding grandiose, the book just might do for Bayesian methods what Apple's original Mac did for the personal computer; here's hoping.

PS. Three worthwhile related (more technical) books:

"Data analysis using regression and multilevel/hierarchical models" by Gelman and Hill. (A very nice book, like "DBDA", but intentionally not-especially-Bayesian).

"Bayesian statistical modeling" by Congdon. (A survey of Bayesian applications).

"Dynamic linear models with R" by Petris et al. Prado and West. (A nice introduction to Bayesian approach to time series).

UPDATE. There is a new kid on the block - "Bayesian modeling using WinBUGS" by Ntzoufras. Although I am still a fan of "DBDA", I think that Ntzoufras's book would be a better bet for many people. Starting with "DBDA", and moving on to that book, may be best.
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26 of 27 people found the following review helpful By R. Dunne on October 3, 2011
Format: Hardcover
All of a sudden it just makes sense! Everyone knows that "lightbulb moment", when previously accumulated knowledge or facts become condensed into a lucid concept, where something previously opaque becomes crystal clear. This book is laden with such moments. This is the most accessible statistics text for a generation and I predict (based on prior knowledge) that it will be a major factor in moving scientists of every shape and size towards the Bayesian paradigm. Even if you're sceptical, you're likely to learn more about frequentist statistics by reading this book, than by reading any of the tomes offered by so called popularisers. If you are a social scientist, laboratory scientist, clinical researcher or triallist, this book represents the single best investment of your time. Bayesian statistics offer a single, unified and coherent approach to data analysis. If you're intimidated by the use of a scripting language like "R" or "BUGS", then don't be. The book repays your close attention and has very clear instructions on code, which elucidate the concepts and the actual mechanics of the analysis like nothing I've seen before. All in all, a great investment. The only serious question that can be raised about the design and implementation of a book such as this is: why wasn't it done before?
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More About the Author

John K. Kruschke is eight-time winner of Teaching Excellence Recognition Awards from Indiana University, where he is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics. He received a Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University.