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Bayesian Inference in Statistical Analysis

Bayesian Inference in Statistical Analysis

George E. P. Box, George C. Tiao

ISBN: 978-1-118-03144-5 January 2011 608 Pages




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.
Nature of Bayesian Inference.

Standard Normal Theory Inference Problems.

Bayesian Assessment of Assumptions: Effect of Non-Normality onInferences About a Population Mean with Generalizations.

Bayesian Assessment of Assumptions: Comparison of Variances.

Random Effect Models.

Analysis of Cross Classification Designs.

Inference About Means with Information from More than One Source:One-Way Classification and Block Designs.

Some Aspects of Multivariate Analysis.

Estimation of Common Regression Coefficients.

Transformation of Data.