Bayesian Data Analysis, Second Edition (Texts in Statistical Science) |  | Authors: Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin
Publisher: Chapman & Hall/CRC
List Price: $73.95
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Rating:  12 reviews
Sales Rank: 35490
Media: Hardcover
Edition: 2
Pages: 696
Number Of Items: 1
Shipping Weight (lbs): 2.3
Dimensions (in): 9.3 x 6.4 x 1.7
ISBN: 158488388X
Dewey Decimal Number: 519.542
EAN: 9781584883883
ASIN: 158488388X
Publication Date: July 29, 2003
Availability: Usually ships in 1-2 business days
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Product Description
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 collectionBayesian 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.
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Showing reviews 1-5 of 12
An introduction to bayesian statstics November 8, 2009
Alessandro Bicci (Italy)
The book introduces the basis of bayesian statistics. There are lots of examples and many applications realized by R software or WinBugs.
Topcis about hierarcical models and MonteCarlo markov Chain method are explained clearly.
I think that a minus prerequisite is a good knowledge of classical stastical inference, stastical models and software packaging as R, stata or Win Bugs.
 I did not care for this book. July 20, 2009
Chad R. Bhatti
3 out of 3 found this review helpful
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.
 Content fantastic, Presentation for Kindle not so much June 12, 2009
DesiLinguist (Greenbelt, Maryland United States)
1 out of 1 found this review helpful
Please note that this review is for the Kindle Book version of the 2nd Edition of this title and is only based on the sample and not the entire book. In addition, the review pertains mostly to the presentation of the content for the Kindle DX rather than the content itself. The content (as represented by the sample) itself is excellent but the Kindle edition leaves a lot to be desired. The greek letters (\theta, \mu etc.) used in the Kindle version are awkwardly much bigger than other mathematical symbols which makes the text unreadable at best. In addition, there are several typos in the mathematical formulae [p(y|y)?] as well as the text.
I am a serious consumer for this book as my research field uses a lot of bayesian analysis. However, I am reluctant to buy this book if it won't be as readable on the Kindle. I would really appreciate it if the publisher would take another look at the Kindle version of the book to fix typos and make the text more presentable, I would buy it in a heartbeat.
Decent for engineers August 29, 2008
John Salvatier (Seattle, WA)
2 out of 2 found this review helpful
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.
 great coverage of Bayesian Methods including MCMC February 13, 2008
Michael R. Chernick (Holland PA)
26 out of 26 found this review helpful
This is a well written text that is fast becoming a classic reference. It contains a wealth of good applications. It is one of the new books that presents the growing use of Bayesian methods in practice since the advancement of Markov Chain Monte Carlo approach. It includes a whole chapter the Markov chain approach to computation. Other strengths of the book include the chapter on missing data and the chapter that provides expert advice. It is one of the best books ever written on the practical aspects of modern Bayesian analysis. I know one of the authors very well (Hal Stern) and am familiar with the fine research work of the others. Don Rubin brings a wealth of knowledge and experience in statistical methods and Bayesian analysis to the table. He is also the inventor of the Bayesian bootstrap.
Another text in the CRC series Markov Chain Monte Carlo in Practice by Gilks, Richardson and Spiegelhalter provides more detail on these methods along with many applications including some Bayesian ones.
Showing reviews 1-5 of 12
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