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Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers


Series: Cambridge Series in Statistical and Probabilistic Mathematics

By: Thomas Leonard and John SJ Hsu

333 pages, Figs, tabs

Cambridge University Press

Paperback | Oct 2001 | #122568 | ISBN: 0521004144
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NHBS Price: £37.99 $50/€43 approx

About this book

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unique in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many new statistical methods, not available elsewhere. A first chapter on Fisherian methods, 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. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practical examples, worked examples, and exercises. It will be essential reading for all statisticians, statistics students, and related interdisciplinary researchers.

'! challenging and worthwhile ! this book is a very welcome and original contribution to the literature on Bayesian statistics.' J. V. Zidek, ISI Short Book Reviews 'The book makes interesting reading and the breadth of ideas tackled by the authors is enormous ! deserves a place in the university library as well as in the personal libraries of researchers who are interested in the Bayesian approach.' Carmen Fernandez, The Statistician 'The book is highly recommended as a well written intermediate book on some modern topics of Bayesian analysis.' H. K. van Dijk, Niew Archief voor Wiskunde 'The explanations stated in the book are very clear, the detailed computations, and the particular way of describing problems, theorems and applications in this book make it useful into only for statisticians but also for other researchers who are confronted with problems of data analysis and who are not primarily familiar with statistical methods.' Monatshefte fur Mathematik 'A very readable and interesting book.' Indian Journal of Statistics


1. Introductory statistical concepts; 2. The discrete version of Bayes' theorem; 3. Models with a single unknown parameter; 4. The expected utility hypothesis and its alternatives; 5. Models with several unknown parameters; 6. Prior structures, posterior smoothing, and Bayes-Stein estimation; Guide to worked examples; Guide to self-study exercises.

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