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Bayesian Data Analysis Hardcover – Nov. 1 2013
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Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
- Four new chapters on nonparametric modeling
- Coverage of weakly informative priors and boundary-avoiding priors
- Updated discussion of cross-validation and predictive information criteria
- Improved convergence monitoring and effective sample size calculations for iterative simulation
- Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
- New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
- ISBN-101439840954
- ISBN-13978-1439840955
- Edition3rd
- PublisherChapman and Hall/CRC
- Publication dateNov. 1 2013
- LanguageEnglish
- Dimensions18.54 x 3.56 x 25.65 cm
- Print length675 pages
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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
"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
Product details
- Publisher : Chapman and Hall/CRC; 3rd edition (Nov. 1 2013)
- Language : English
- Hardcover : 675 pages
- ISBN-10 : 1439840954
- ISBN-13 : 978-1439840955
- Item weight : 1.32 kg
- Dimensions : 18.54 x 3.56 x 25.65 cm
- Best Sellers Rank: #150,567 in Books (See Top 100 in Books)
- #90 in Counselling Research (Books)
- #114 in Statistics Textbooks
- #180 in Probability & Statistics (Books)
- Customer Reviews:
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It's worth only as a reference and mainly as a practical guide. Most methods are barely explained, no theoretical foundations of why they work is given. If you're looking for a recipe book, then this is it. However, even at that, it's not suitable for every subject. In econometrics, this book would be pretty much pointless. Much of the topics we're interested do not show up...
On the good side, some exercises have solutions on their site. But even then, these exercises aren't the most enticing I've ever seen. Example: In chapter 2, there's an exercise on non-infor priors. They say to choose one and apply to the problem. The solutions at the author's site say that any of those referenced in the book work. Well, choose Jeffreys. Then, you'll get a negative fisher information scalar. Hum... this can't be. I post a question on CV stackexchange, and then I discover that Jeffreys prior doesn't always work when you have a discrete r.v. And the book is silent on this matter. There are more examples like this... It's only useful for those already proficient at the subject, or looking just as rough practical guide for biostatistics.

If you happen to be looking for a simpler introduction then perhaps consider the book by John Kruschke. If you are not convinced about Bayesian statistics yet, consider E.T. Jaynes' wonderful book on Probability Theory from a more philosophical perspective.

Since the 2nd edition came out there have been substantial improvements in MCMC computation algorithms and convergence modelling as well in Bayesian nonparametric modelling. Substantial new material has been added to cover these items. The one potential caveat is that the authors have stripped out all the BUGS code that was in the previous two editions and replaced it with code in their new language, Stan. They claim Stan is better (faster, better convergence in certain situations where BUGS is known to struggle) but BUGS is proven technology whereas Stan is a (very promising) newcomer. There's more than enough new material to justify upgrading to edition 3 in my view.

