Synopsis
The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wide variety of problems in data analysis and decision-making have become feasible. The purpose of this volume is to present several detailed examples of the applications of Bayesian methods. The emphasis of each article is on the scientific or technological context of the problem being solved, and much background material is provided to complete the description of the analysis. This collection illustrates the ways in which Bayesian methods are permeating statistical practice. Noteworthy in the articles are the construction of explicit and conceptually simple models, the use of information other than the data under analysis, and the representation of uncertainty from various sources in the model.
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Review
From the reviews:
TECHNOMETRICS
"...well written and can be appreciated by those with little biology background...In general, I found many of the papers collected in this book interesting...readers who are interested in applying Bayesian analysis in real case studies may find this book useful. One might consider using this book as supplementary material in graduate-level seminar courses on Bayesian data analysis."
Journal of the American Statistical Association, June 2004
"The volume as a whole illustrates the range of models that can now be fitted following the Markov chain Monte Carlo revolution, assess the behavior of posterior summaries under model misspecifications."
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