Bayesian Data Analysis, Third Edition by Andrew Gelman and John B. Carlin

Bayesian Data Analysis, Third Edition by Andrew Gelman and John B. Carlin
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Bayesian Data Analysis, Third Edition by Andrew Gelman and John B. Carlin

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求all leaders in the statistics community求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.



Editorial Reviews 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." 求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. 求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. 求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 求David Blackwell, University of California, Berkeley, USA About the Author Andrew Gelman is a Professor of Statistics and Political Science at Columbia University, New York. He received the Presidents' Award in 2003, which is awarded each year to the best statistician under forty. He has written about 200 research articles on statistical methods, teaching, and applications, and his books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, Applied Regression and Multilevel Models, and, most recently, Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do. He is the founding director of the Quantitative Methods in Social Sciences Program, an interdisciplinary program at Columbia University that bridges history, economics, sociology, political science, psychology, and statistics.

Carlin, University of Melbourne, Australia.

Stern, Iowa State University, Ames.

Donald B. Rubin is John L. Loeb Professor of Statistics at Harvard University, where he has been professor since 1983 and department chair for thirteen of those years. He has authored or coauthored nearly four hundred publications (including ten books), has four joint patents, and has made important contributions to statistical theory and methodology, particularly in causal inference, design and analysis of experiments and sample surveys, treatment of missing data, and Bayesian data analysis. Rubin has received the Samuel S. Wilks Medal from the American Statistical Association, the Parzen Prize for Statistical Innovation, the Fisher Lectureship, and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. He was named Statistician of the Year by the American Statistical Association, Boston and Chicago chapters. He is one of the most highly cited authors in mathematics and economics with nearly 150,000 citations to date.