- 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: 36 customer reviews
- Amazon Best Sellers Rank: #24,860 in Books (See Top 100 in Books)
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Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) 3rd Edition
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"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
Top customer reviews
The book has a lot of good content and assumes previous knowledge on basic probability and statistics.
Definitely recommended as a starter, refresher, self-study guide, textbook or even reference for anyone interested in bayesian modelling.
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.