Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) [Hardcover]

Andrew Gelman (Author), John B. Carlin (Author), Hal S. Stern (Author), Donald B. Rubin (Author)

Book Description

Publication Date: July 29, 2003 | ISBN-10: 158488388X | ISBN-13: 978-1584883883 | Edition: 2

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:

• Stronger focus on MCMC
• Revision of the computational advice in Part III
• New chapters on nonlinear models and decision analysis
• Several additional applied examples from the authors' recent research
• Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more
• Reorganization of chapters 6 and 7 on model checking and data collection

Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Editorial Reviews

Review

If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice… This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.
-John Grego, University of South Carolina

Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods
-Prof. David Blackwell, Department of Statistics, University of California, Berkeley

Praise for the first edition:
A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life.
-Robert Matthews, Aston University, in New Scientist

an essential reference text for any applied statistician
-Stephen Brooks, University of Cambridge, in The Statistician

will contribute to closing the gap between scientists and statisticians
-Sander Greenland, UCLA, in American Journal of Epidemiology

an excellent teaching reference for advanced undergraduate and graduate courses
-Nicky Best, Imperial College School of Medicine, in Statistics in Medicine

Product Details

• Hardcover: 696 pages
• Publisher: Chapman and Hall/CRC; 2 edition (July 29, 2003)
• Language: English
• ISBN-10: 158488388X
• ISBN-13: 978-1584883883
• Product Dimensions: 6.1 x 1.6 x 9.2 inches
• Shipping Weight: 2.3 pounds (View shipping rates and policies)
• Average Customer Review: 3.6 out of 5 stars  See all reviews (17 customer reviews)
• Amazon Best Sellers Rank: #173,780 in Books (See Top 100 in Books)

Most Helpful Customer Reviews

105 of 121 people found the following review helpful

3.0 out of 5 stars A good introductory book, but... January 25, 2005

By Zoro

Format:Hardcover

I read the other reviews and agree with them to some extent. This is

a good introduction to applied Bayesian analysis. Lots of

good examples, illustrations and exercises.

If you are the kind of person who learns by way of examples, then

this might be the text book for you. If you are looking for the

bigger picture, then you will be lost here. There is very little in the way

of theory. Why is this the right method? What is gained theoretically

over a frequentist method? What are the theoretical properties of the

proposed approach? To a large extent these kinds of questions remain a mystery.

In terms of flexibility an applied Bayesian approach has some decided

advantages. However, in terms of theory

it's almost as if the authors want you to believe that once

you adopt the Bayesian approach then the benefits of averaging

by way of using a prior will always be the right thing to do.

You could argue that advanced questions like this are better suited for

a more advanced text book. I tend to ask more out of a book.

21 of 23 people found the following review helpful

5.0 out of 5 stars Very Excellent, but non-statisticians should start elsewhere June 5, 2006

By MrDNA

Format:Hardcover

Gelman's book is an excellent and complete introduction to Bayesian methods. It covers a number of topics not touched by other intros I've read, and focuses much more on regression and ANOVA than other texts.

There are two downsides, coming from someone in psychology. First, the book seems to hover between an introductory text and a more advanced one. The topics covered are mostly introductory, but the examples aren't always entirely easy to follow. A tighter integration with the R and Bugs code would help. Perhaps a section at the end of the chapters containing a code example for each topic would be ideal. It's not that the topics themselves are necessarily opaque, but Gelman moves too fast at times, making it hard to think in terms of notation, theory, experimental design AND code at the same time (for those of us constantly thinking about how this affects our own research).

Second, as a general rule, this book is outside the ken of most psychologists. This is unfortunate since the methods are ideal for our discipline, and since many psychologists already perceive a large barrier of entry to statistics. As a psychologist with minimal undergraduate training in stats, I would (and did) start with a standard statistics book like Casella and Berger, and then move on to a gentler introduction to Bayesian methodology, like _Bayesian Methods: A Social and Behavioral Sciences Approach_ by Jeff Gill. Also, you can barely do anything in this book with SPSS so you'll have to learn R and Bugs.

23 of 27 people found the following review helpful

2.0 out of 5 stars I did not care for this book. July 19, 2009

By Chad R. Bhatti

Format:Hardcover

I used this book for an introductory graduate course in Bayesian Data Analysis. I found aspects of the book to be needlessly confusing due to a lack of mathematical clarity in the text. The mathematical level of this book is very low. However, the book proceeds to perform Bayesian data analysis using multivariate normal theory and generalized linear models, without developing any background. It seems contradictory to assume such a low mathematical level, but also assume that the reader knows particular results from multivariate normal theory and glm. The verbal orientation of the book can be frustrating, especially since a verbal description could adequately suggest more than one model formulation. I would not recommend this book as a text book. This book seems best served as an auxiliary book for examples. If you want to learn Bayesian statistics, you need to buy "The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation" by Christian P. Robert. Robert's book is the correct place to start.