Bayesian Data Analysis

Bayesian Data Analysis

Hardback Texts in Statistical Science

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

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  • Publisher: Chapman & Hall/CRC
  • Format: Hardback | 696 pages
  • Dimensions: 163mm x 236mm x 43mm | 1,043g
  • Publication date: 29 July 2003
  • Publication City/Country: Boca Raton, FL
  • ISBN 10: 158488388X
  • ISBN 13: 9781584883883
  • Edition: 2, Revised
  • Edition statement: 2nd Revised edition
  • Illustrations note: 91 black & white illustrations, 48 black & white tables
  • Sales rank: 143,022

Product description

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

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Review quote

"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. 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 "Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods" -Prof. David Blackwell, Department of Statistics, University of California, Berkeley Praise for the first edition: "A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life." -Robert Matthews, Aston University, in New Scientist "an essential reference text for any applied statistician" -Stephen Brooks, University of Cambridge, in The Statistician "will contribute to closing the gap between scientists and statisticians" -Sander Greenland, UCLA, in American Journal of Epidemiology "an excellent teaching reference for advanced undergraduate and graduate courses" -Nicky Best, Imperial College School of Medicine, in Statistics in Medicine

Table of contents

FUNDAMENTALS OF BAYESIAN INFERENCE Background Single-Parameter Models Introduction to Multiparameter Models Large-Sample Inference and Connections to Standard Statistical Methods FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Hierarchical Models Model Checking and Improvement Modeling Accounting for Data Collection Connections and Controversies General Advice ADVANCED COMPUTATION Overview of Computation Posterior Simulation Approximations Based on Posterior Modes Topics in Computation REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference and Sensitivity Analysis Analysis of Variance SPECIFIC MODELS AND PROBLEMS Mixture Models Multivariate Models Nonlinear Models Models for Missing Data Decision Analysis APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Example of Computation in R and Bugs References