Selected Product:
| Bayesian Data Analysis, Second Edition
Hardcover
Edition: 2
Author: Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin
Publisher: Chapman & Hall/CRC
Release Date: 2003-07-29
ISBN-10: 158488388X
ISBN-13: 9781584883883
List Price: $69.95
Average Customer Rating:
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Summaries and Customer Reviews are supplied by Amazon.com
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 critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, added chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models, generalized linear mixed models, and more. The book is an introductory text and a reference for working scientists throughout their professional life.
Decent for engineers
| Customer Rating: | 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
| Customer Rating: | 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.
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Comprehensive, but not well-written
| Customer Rating: | | This book is a very comprehensive treatment of Bayesian data analysis. However, it is not well-written. I find Lancaster's book to be much more well-written and interesting to read. |
Very Excellent, but non-statisticians should start elsewhere
| Customer Rating: | 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. |
As Good As It Gets For An Intro To Bayes
| Customer Rating: | | Yes, it is an introduction to Bayesian methods. That means you have to have a very good understanding of classical statistics (at the level of Casella and Berger would be optimal) and then be willing to use the WinBugs program to further your knowledge. A great book. |
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