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Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition

24 customer reviews
ISBN-13: 978-1584883883
ISBN-10: 158488388X
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Editorial Reviews


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

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Product Details

  • Series: Chapman & Hall/CRC Texts in Statistical Science (Book 106)
  • 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 1.4 x 9.2 inches
  • Shipping Weight: 2.3 pounds
  • Average Customer Review: 3.4 out of 5 stars  See all reviews (24 customer reviews)
  • Amazon Best Sellers Rank: #675,598 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

116 of 135 people found the following review helpful By Zoro on January 25, 2005
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.
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30 of 32 people found the following review helpful By MrDNA on June 5, 2006
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.
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28 of 33 people found the following review helpful By A Buyer on July 19, 2009
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.
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6 of 6 people found the following review helpful By John Salvatier on August 29, 2008
Format: Hardcover
This seems to be the best book out there for learning Bayesian statistics. The book is well written and usually quite clear. I think it be better organized, and pointers to programming examples would be welcomed, especially in the introductory computation section.

I am an engineer, and unfortunately for me, this book is geared towards social scientists. However, no other bayesian statistics books currently teach from an engineering perspective, so this is your best be if you are an engineer.

This book does assume a good deal of familarity with mathematical statistics, which many engineers do not have. However, it is possible to get though it by looking this up on wikipedia.
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8 of 9 people found the following review helpful By K. Dixon on May 4, 2010
Format: Hardcover Verified Purchase
I've read many, many books on Bayesian analysis at this point. Gelman's book is head-and-shoulders above the rest as an introduction to Bayesian analysis. His book does an outstanding job of introducing the model and the motivation behind the parameters from a very intuitive perspective. Many other Bayesian statisticians seem to enjoy the formulation of the problem so much (count myself as one of them) that they get lost in the beauty of the math and it becomes difficult to effectively convey why the model was selected and how to infer the parameters.

Gelman's book is the first book I've read that strikes a balance between the formulation and the explanation.

This book is not for those looking for the theoretical motivation behind Bayesian analysis, or those interested in absorbing the bounds of asymptotic performance, etc. Christian Robert's "The Bayesian Choice", or his other co-authored books, is a much better place for those who have already gotten their minds around Bayesian statistics and want to explore the gory details.

I don't dock Gelman's book for the limited amount of formal propositions/theorems/proofs because I feel that there are plenty of other decent books that do that well. But Gelman's book fills a much-needed gap for those interesting in starting out in Bayesian statistics.
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