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Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Hardcover – November 1, 2013

ISBN-13: 978-1439840955 ISBN-10: 1439840954 Edition: 3rd

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Product Details

  • Series: Chapman & Hall/CRC Texts in Statistical Science (Book 106)
  • Hardcover: 675 pages
  • Publisher: Chapman and Hall/CRC; 3 edition (November 1, 2013)
  • Language: English
  • ISBN-10: 1439840954
  • ISBN-13: 978-1439840955
  • Product Dimensions: 1.2 x 7.5 x 10.2 inches
  • Shipping Weight: 2.9 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (20 customer reviews)
  • Amazon Best Sellers Rank: #10,426 in Books (See Top 100 in Books)

Editorial Reviews

Review

"The second edition was reviewed in JASA by Maiti (2004) … we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. … this being a third edition begets the question of what is new when compared with the second edition? Quite a lot … this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis."
—Christian P. Robert, Journal of the American Statistical Association, September 2014, Vol. 109

Praise for the Second Edition
… it is simply the best all-around modern book focused on data analysis currently available. … There is enough important additional material here that those with the first edition should seriously consider updating to the new version. … when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice.
—Lawrence Joseph, Montreal General Hospital and McGill University, Statistics in Medicine, Vol. 23, 2004

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, USA

… easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods
—David Blackwell, University of California, Berkeley, USA


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Customer Reviews

4.8 out of 5 stars
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This book is not only comprehensive - it is also well organized and extremely well written.
J. Schneider
The book starts with basic principles, describes current approaches to Bayesian modeling, computational statistics and related fields.
Subir Singh
This, along with the math requirements, makes the book suited for the graduate student and/or practitioner.
J. Leeman

Most Helpful Customer Reviews

15 of 16 people found the following review helpful By Wayne Folta on December 7, 2013
Format: Hardcover Verified Purchase
What can you say when a classic like this is updated? The original was THE reference on the topic and this one expands on it and adds all kinds of little things they've thought about over the last 15+ years.

They've added chapters on Basis Function models, Gaussian Process models, Finite Mixture models, and Dirichlet Process models, and also lots of important but small concepts that we've previosly seen only in places like Andrew's blog, including things like boundary-avoiding priors. The coding example Appendix C has also been reworked to use Stan rather than BUGS.

The physical layout of the book has been improved as well. It's the same thickness, but slightly larger in the other two dimensions and with a smaller bottom margin, which I think gives a much better amount of information per page. The only thing I could ask for layout-wise is to have chapter/section numbers at the top of each page to make it quicker to find something.
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7 of 7 people found the following review helpful By Nathan A. Edwards VINE VOICE on October 14, 2014
Format: Hardcover Vine Customer Review of Free Product ( What's this? )
It seems that I am in the minority among reviewers in that this is my first experience with any iteration of this text. I will also admit that I am in the unusual demographic of applied statistics hobbyists. After reading the reviews of others, I realized that if an understanding of the Bayesian approach is to be pursued, this is the text to have.

I don't mind saying that I was intimidated by how quickly the text ramps up from introduction to application. I suffered no shortage of having to reference outside material to catch my bearings, but this is due to my own unorthodox path to this text. It is probably only due to the repeated use of real-world examples and a down-to-earth writing style that I am able to keep up at all.

Realizing how little I know, and how much I am able to glean from this text - which should be way beyond my comprehension - I can confidently say that this text is about as accessible as it gets without watering down the subject. I can't even imagine what a boon this text must be to those who have a broader educational foundation to build upon!
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10 of 11 people found the following review helpful By ls on June 23, 2014
Format: Kindle Edition Verified Purchase
It's not possible to read pages side-by-side on Kindle for PC. "Appendix A, Table A.1" is laid out across left and right pages in the 2nd edition. The 3rd (Kindle) edition can be viewed only page by page (affects new Table A.1 entries for Lognormal, LKJ correlation, Logistic, Log-logistic distributions). BTW, old people like me need to read digital text on large screens -- or wear reading glasses. I'm probably going to return my Kindle version of the 3rd edition for the hardcover (it's that good!). I didn't download the Kindle sample, but I doubt that the sample would have revealed the side-by-side page reading problem unless "Appendix A, Table A.1" was part of the sample.
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9 of 10 people found the following review helpful By Seth M. Spain on December 7, 2013
Format: Hardcover
This book continues the very high standard set by the first and second edition. With expanded coverage of weakly informative priors and nonlinear and nonparametric methods (as well as a strong appendix covering the authors' "stan" package for probabilistic modeling), this belongs on the shelf of even owners of one of the previous editions. Just great, definitive, even. An excellent book for both academics and practitioners.
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
This book provides a short one chapter introduction to the use of Bayesian Analysis suitable for someone who has completed elementary statistics in an undergraduate program. The body of the book which is well illustrated by examples requires a significant background in statistics, calculus and linear algebra. Graphics and application examples are provided for these topics.

One physical criticism of the book (which may be limited to my copy and a given batch), is the light grey type making up much of the book. One would expect a high level of legibility in an expensive reference work.
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1 of 1 people found the following review helpful By Subir Singh TOP 500 REVIEWERVINE VOICE on September 22, 2014
Format: Hardcover Vine Customer Review of Free Product ( What's this? )
The book is the 3rd edition of the authority on Bayesian Data Analysis. The authors have designed the book to be useful to all levels of users - that is, all level of students of the subject.

The book starts with basic principles, describes current approaches to Bayesian modeling, computational statistics and related fields. It also highlights Bayesian methods in Applied statistics. What keeps this book interesting is the author's focus on practice rather than philosophy. The forte of the book is in its inclusion of worked examples from real life applications.

The 3rd edition adds 4 new chapters to the 2nd edition - Basis Functions, Gaussian Process Models, Finite Mixture Models and Dirichlet Process Models.

The most impressive feature of the text is that despite its magnitude, it is beneficial to all level of users - from students starting off in statistical analysis, to graduate students looking to leverage effective approaches to a reference guide for statisticians in enterprise.

Highly Recommended!
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2 of 3 people found the following review helpful By Harkius VINE VOICE on October 8, 2014
Format: Hardcover Vine Customer Review of Free Product ( What's this? )
This has been updated, and is unquestionably a standard in the field. If you do work on probability, you should have a copy of this on your bookshelf. If nothing else, you can use it to kill small animals. But you'll probably want to use it for something more intellectual. I admittedly didn't sit down and read the whole thing, since it's a reference book, not a work of fiction. But my casual perusal suggests that it is broad, deep, and very, very well written.

Representing some of the most up-to-date methodologies in statistics (i.e., three hundred years old), Bayesian analysis is an important tool in the kit of any statistician. I know this to be true, despite the fact that I view Bayesian statistics with a deep, deep distrust. And I'm not alone. From Wikipedia: "Bayesian probability is the name given to several related interpretations of probability, which have in common the notion of probability as something like a partial belief, rather than a frequency." I'm a devout Frequentist, but mostly because I think that assumptions probably shouldn't go into the conclusion that you're testing to determine whether the assumption is correct. That kind of seems logical to me. And to turn it around and do it the other way feels like…well…the opposite of logic. Choose your term, illogic, magic, nonsense. They all seem to apply, even if Bayesian methodologies are quite popular and well-accepted.

But I guess, if you're going to do magic, you might as well get a really good grimoire. And this would be that. So enjoy!
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