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

by Andrew Gelman (Author), John B. Carlin (Author), Hal S. Stern (Author),
3.5 out of 5 stars 25 customer reviews
ISBN-13: 978-1584883883
ISBN-10: 158488388X
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Product Details

  • Series: Chapman & Hall/CRC Texts in Statistical Science (Book 106)
  • Hardcover: 690 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: 3.5 out of 5 stars  See all reviews (25 customer reviews)
  • Amazon Best Sellers Rank: #524,288 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

3.0 out of 5 starsA good introductory book, but...
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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2.0 out of 5 starsI did not care for this book.
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.
3 Comments 28 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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5.0 out of 5 starsTurns potentially dry material in something interesting
By Gus Harlow on April 5, 2010
Format: Hardcover Verified Purchase
This book, more than any statistics book that i've read, is full of beautiful heuristic commentary which is also useful to a practitioner.
I've found that an excellent supplement is "Bayesian Modeling Using WinBugs" by Ntzoufras. Gelman et al. provide wordy but enlightening explanations of Bayesian concepts with just the right amount of Math for someone that wants get their hands dirty and analyze some data with competance. The book is full examples with nice discussions from someone with a deep understanding of statistical inference. What these examples sometime lack is details of how the results were got.

This is where Ntzoufras comes in.

Ntzoufras complements Gelman perfectly by offering a book full of detailed examples with a lot of R and WinBugs code.
Jim Albert's book, "Bayesian Computation with R" is also a very good supplement to Part III of Gelman et al. as is Albert's LearnBayes R package.

Gelman has also co-written an R package called "arms" which can also supplement some of this book.
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1.0 out of 5 starsOne star and Half
By The Prof on February 12, 2014
Format: Hardcover
I read large portion of the book and I still don't really understand why this book is popular. Not sure to whom the book is targeting. Senior undergrad or grad Statistics Students ?. No. Scientists ?. The answer is also No. The advanced techniques are not explained enough even for a graduate student. Examples are just so awful and complicated (Just try the linear regression one) for someone who wants to learn the subject for the first time. Don't even think about it if your not so familiar with the frequnetist approach.Practical prior elicitation is almost null. They don't show how to extract and use information to induce prior distribution on the parameters. One of strengths of Bayesian Analysis is just ignored in favor of "ask the expert and that's it".

I am giving this extra half for chapters 1-5. I like these chapters. They mentioned good points.

Update: I just finished reading a chapter about Mixture models. I just needed a good introduction about the topic from Bayesian perspective. I am just lost now and have worse understanding for the material than before reading their chapter. It is not useful at.
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2.0 out of 5 starsBaysian for US jocks
By Dr Heather E Mitchell on March 14, 2015
Format: Hardcover Verified Purchase
This book is written for American jocks. Far too many examples feature American football. I have no interest and little knowledge of this game so every time I start to read it I become annoyed, frustrated and give up. Th authors should consider how they would feel about a text where most of the examples were based on Chinese classical dance.
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4.0 out of 5 starsVery informative, could use more explicit calculations
By Dylan on July 8, 2011
Format: Hardcover Verified Purchase
This is a book on Bayesian data analysis, but should not be someone's first book on Bayesian methods. It should also not be someones first book on statistical modeling.

The authors do a good job of building up from simple models to ones with more and more generalization. They also do a good job of adding in real life data sets, and walking you through how they modeled the data sets, verified the results, sampled various posterior distributions, etc.

The one aspect of the book that I found a little unbalanced was that it was wordier than mathematical/coded. In one sense that's a virtue of the authors, many mathematics books drill you with hundreds of pages of dense math, and I'm sure by avoiding that path, the book's audience is larger. But I still found myself, particularly when first being introduced to a concept, wanting to see an explicit simple calculation, just to make sure that I fully understood the basic concept.

Along the same lines of the past paragraph, I found Albert's 'Bayesian Computation with R' to be a good supplement. It is rich in code, and thin in text, so the two books balance each other well. I typically found myself reading a chapter in this text, then finding the associated chapter in Albert's book, then coding up some additional examples on my own.

All and all good stuff. I'd give it 4.5 stars, and will flip a coin to see if that ends up being recorded as a 4 or 5! tails it was...
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Most Recent Customer Reviews

5.0 out of 5 starsFive Stars

great read.

Published 11 hours ago by Amazon Customer
5.0 out of 5 starsFive Stars

Great

Published 18 months ago by Ariya
5.0 out of 5 starsnice book

The book was in the best condition. It is almost a new one. It is better than I expected. Thanks.

Published on April 4, 2014 by Daeha Kim
1.0 out of 5 starsDon't purchase the Kindle edition

The transcription of formulae in the Kindle edition is unusably bad, especially when in-line. Problems include missing accents over variables, missing or mis-transcribed operators,... Read more

Published on November 10, 2013 by Matthew D. Haase
2.0 out of 5 starsWanna know Bayesian? Get another one.

This book is a famous one. However I fail to see why. It is neither theoretical enough to give deep insight, nor practical and broad enough to cover most useful tools and... Read more

Published on March 3, 2013 by Michael
2.0 out of 5 starsPoor Motivation

This book is completely inadequate in my view. I'm using this text for a graduate course in Bayesian Stat. and it is extremely frustrating. Read more

Published on February 16, 2013 by Alex
2.0 out of 5 starsReview of Kindle Edition/Translation

I have visited the site at least five times to purchase the kindle edition, but each time I have been discouraged by the translation to the kindle format. Read more

Published on November 28, 2010 by shubhomoy
5.0 out of 5 starsExcellent book

Covers almost all you need to know about Bayesian statistics.

Book in good quality. Fast delivery.

Published on April 15, 2010 by Haowen Zhong
3.0 out of 5 starsbayesian data analysis

unreadable on Kindle. Greek letters too large, latin letters confused (e.g., p(y|y)). What a pity.

Published on December 19, 2009 by Alison Hobbs

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