Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers

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A Bayesian "posterior distribution" or "predicative distribution" summarizes everything you need to know about an unknown parameter, or future observations. This unique book shows how to use Bayesian statistical techniques in a sound and practically relevant manner. It will guide the reader on inferring scientific, medical, and social conclusions from numerical data. The authors explain the subtle assumptions needed for Bayesian methodology and show how to use them to obtain good-quality conclusions. The methods also perform remarkably well in terms of computer-simulated frequency properties.

The lively introductory chapter on Fisherian methods (the frequency approach), together with a strong overall emphasis on likelihood, makes the text suitable, for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. A chapter on advances in utility theory, and several sections on time series and forecasting, makes the text also suitable for quantitative economics students. The other chapters contain material on the linear model, categorical data analysis, survival analysis, random-effects models, and nonlinear smoothing. The book contains numerous worked examples, self-study exercises, and practical applications. It provides essential reading for final-year undergraduates, Masters-degree and graduate students, statisticians, and other interdisciplinary researchers wishing to develop good-quality conclusions from their data and to pursue the notion of scientific truth.

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Editorial Reviews

From the Publisher
"The book provides a solid, well-written introduction to the basic tenets of Bayesian modeling, and deserves serious consideration for adoption for a graduate-level introduction to Bayesian methods." Journal of the American Statistical Association

"This book provides excellent up-to-date coverage of modern Bayesian statistics...clearly written and at a reasonably high level." Mathematical Reviews

"Bayesian Methods is pregnant with detailed examples, pulled primarily from recent literature, especially from contributions by the authors. Rather than serving simply as illustrations of results in the text, these examples are an integral part of the authors' development. What is more, they are interesting. They endow the theory with life and draw the reader deeper into the text. I strongly recommend this book to anyone interested in Bayesian methods. I look forward to using it in the classroom." Technometrics

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Product Details

Table of Contents

1 Introductory Statistical Concepts 1
2 The Discrete Version of Bayes' Theorem 75
3 Models with a Single Unknown Parameter 98
4 The Expected Utility Hypothesis 165
5 Models with Several Unknown Parameters 189
6 Prior Structures, Posterior Smoothing, and Bayes-Stein Estimation 242
References 303
Author Index 321
Subject Index 326
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