An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.
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An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.
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39 of 40 people found the following review helpful:
4.0 out of 5 stars
the best of several Bayesian texts by Press,
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This review is from: Bayesian Statistics: Principles, Models, and Applications (Wiley Series in Probability and Statistics) (Hardcover)
Jim Press is Professor of Statistics at the University of California at Riverside. He has authored a number of fine books on Bayesian statistical methods. This one, however, is my favorite because he very clearly lays out the principles of the Bayesian paradigm, develops the models that are useful in applications and then provides many interesting examples of the applications. This book does not require advanced knowledge of statistics or any previous knowledge about Bayesian methods.
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