Bayesian Data Analysis
3rd Edition
Published on 1. November 2013
Book
Hardback
675 pages
978-1-4398-4095-5 (ISBN)
Description
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.
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.
Reviews / Votes
"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
More details
Series
Edition
3rd edition
Language
English
Place of publication
Boca Raton
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Undergraduate Core
Illustrations
121 s/w Abbildungen, 49 s/w Tabellen
49 Tables, black and white; 121 Illustrations, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 41 mm
Weight
1457 gr
ISBN-13
978-1-4398-4095-5 (9781439840955)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Andrew Gelman | John B. Carlin | Hal S. Stern
Bayesian Data Analysis
E-Book
11/2013
3rd Edition
Chapman & Hall/CRC
€116.99
Available for download
Andrew Gelman | John B. Carlin | Hal S. Stern
Bayesian Data Analysis
E-Book
11/2013
3rd Edition
Chapman and Hall
€116.99
Available for download
Previous edition
Andrew Gelman | John B. Carlin | Hal S. Stern
Bayesian Data Analysis, Second Edition
Book
07/2003
2nd Edition
Chapman & Hall/CRC
€89.32
Article exhausted; check for reprint
Persons
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin
Content
Fundamentals of Bayesian Inference. Fundamentals of Bayesian Data Analysis. Advanced Computation. Regression Models. Nonlinear and Nonparametric Models. Appendices.