Paperback. 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. Designed to form the basis of a graduate course on Bayesian inference, this textbook discusses important general issues of the Bayesian approach. It investigates problems, illustrating the appropriate analysis of mathematical results with numerical examples. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Bayesian Inference in Statistical Analysis (Paperback)
Editor: John Wiley & Sons Inc, New York
Fecha de publicaci�n: 1992
Idioma: Ingl�s
ISBN 10: 0471574287
ISBN 13: 9780471574286
Encuadernaci�n: Paperback
Edici�n: 1� Edici�n
Estado: new

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Sinopsis

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.

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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.

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