Bayesian Data Analysis, Second Edition

Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin

July 29, 2003 by Chapman and Hall/CRC
Textbook - 690 Pages - 91 B/W Illustrations
ISBN 9781584883883 - CAT# C388X
Series: Chapman & Hall/CRC Texts in Statistical Science

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Features

  • Provides a thorough update of the groundbreaking text in Bayesian statistics written by the major players in the field
  • Describes the principles of Bayesian analysis, emphasizing practical rather than theoretical issues
  • Guides readers through the entire process of Bayesian analysis using real, applied examples
  • Considers a variety of models, including linear regression, hierarchical (random effects) models, robust models, generalized linear models, and mixture models
  • Addresses issues ranging from incorporating survey design information to checking model adequacy, to handling missing data-all from a consistent and pragmatic Bayesian perspective
  • Summary

    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.