Bayesian Inference in Statistical Analysis

Box, George E. P.; Tiao, George C.

ISBN 10: 0471574287 / ISBN 13: 9780471574286

Published by Wiley-Interscience, 1992

About this Item

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Bibliographic Details

• Title: Bayesian Inference in Statistical Analysis
• Publisher: Wiley-Interscience
• Publication Date: 1992
• Language: English
• ISBN 10: 0471574287
• ISBN 13: 9780471574286
• Binding: Soft cover
• Condition: good

About this title

Synopsis

Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

About the Author

GEORGE E. P. BOX, PhD, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin, Madison. His lifelong work has defined statistical analysis, while his name and research is a part of some of the most influential statistical constructs, including Box & Jenkins models, Box & Cox transformations, and Box & Behnken designs. Dr. Box is the coauthor of a number of Wiley books, including most recently, Statistical Control by Monitoring and Adjustment, Second Edition; Response Surfaces, Mixtures, and Ridge Analyses, Second Edition; and Improving Almost Anything: Ideas and Essays, Revised Edition.

NORMAN R. DRAPER is professor emeritus at the University of Wisconsin, Madison, in the Department of Statistics. His research interests include Experimental Design, Linear Models, and Nonlinear Estimation.