Synopses & Reviews
Bayesian statistics directed towards mainstream statistics. How to infer scientific, medical, and social conclusions from numerical data.
"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
Describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students as well as statistical and interdisciplinary researchers. It is unique in presenting Bayesian statistics with an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data.
This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. The first chapter presents a comparison with traditional Fisherian methods. Subsequent chapters relate Bayesian methods to many areas of statistics, for instance, 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.
Table of Contents
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.