Bayesian inference

著者

書誌事項

Bayesian inference

Anthony O'Hagan and Jonathan Forster

（Kendall's advanced theory of statistics / Alan Stuart & J. Keith Ord, v. 2B）

Arnold, 2004

2nd ed

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注記

Distributed in the U.S.A. by Oxford University Press, New York

Includes bibliographical references (p. [441]-470) and indexes

内容説明・目次

内容説明

Kendall's objective in setting out to write the original Kendall's Advanced Theory of Statistics, published in two volumes in 1943 and 1946, was 'to develop a systematic treatment of [statistical theory] as it exists at the present time.' With this aim in mind, the first edition of Bayesian Inference was added as Volume 2B of the Kendall's Advanced Theory of Statistics in 1994, to introduce the new and rapidly growing field of Bayesian statistics. This new edition is a response to the developments and advances that have taken place in this area over the last few years and offers the reader an up-to-date and comprehensive overview of Bayesian statistics. The second edition of Bayesian Inference has been expanded to include new chapters on Markov Chain Monte Carlo methods, discrete data models and non-parametric models. Existing chapters have also been thoroughly revised and updated and there is greater coverage of computational methods and of model comparison and criticism. There is also a new chapter of case studies, providing practical illustrations of the theory presented throughout the book.Like the other volumes in the Kendall's Library of Statistics, the first edition of Bayesian Inference provided a good selection of exercises at the end of each chapter. This popular feature is retained in the new edition, with many new exercises to deepen the reader's understanding. Clearly written and offering a wide-ranging introduction to Bayesian statistics, this book will be an essential reference source for students, researchers and practitioners in statistics.

目次

  • The Bayesian method
  • inference and decisions
  • general principles and theory
  • subjective probability
  • non-subjective theories
  • prior distributions
  • model comparison
  • robustness and model criticism
  • computation
  • Marcov Chain Monte Carlo
  • the linear model
  • generalized linear models
  • nonparametric models
  • other standard models
  • short case studies.

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