Bayesian Data Analysis

Bayesian Data Analysis

Hardback Chapman & Hall/CRC Texts in Statistical Science

By (author) Andrew Gelman, By (author) John B. Carlin, By (author) Hal S. Stern, By (author) David B. Dunson, By (author) Aki Vehtari, By (author) Donald B. Rubin

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  • Publisher: Chapman & Hall/CRC
  • Format: Hardback | 675 pages
  • Dimensions: 183mm x 257mm x 38mm | 1,315g
  • Publication date: 7 November 2013
  • ISBN 10: 1439840954
  • ISBN 13: 9781439840955
  • Edition: 3, Revised
  • Edition statement: 3rd Revised edition
  • Illustrations note: 121 black & white illustrations, 49 black & white tables
  • Sales rank: 74,675

Product 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.

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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

Table of contents

FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.