Bayesian Data Analysis, Third Edition Author: Andrew Gelman | Language: English | ISBN:
B00I60M6H6 | Format: EPUB
Bayesian Data Analysis, Third Edition Description
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.
- File Size: 16680 KB
- Print Length: 675 pages
- Publisher: Chapman and Hall/CRC; 3 edition (January 31, 2014)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00I60M6H6
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #123,184 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #58
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics
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.
By Wayne Folta
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.
By Seth M. Spain
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