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Bayesian Inference

Bayesian Inference

Nicholas G. Polson , George C. Tiao

Edited by Nicholas G. Polson, Assistant Professor of Statistics and George C. Tiao, W. Allen Wallis Professor of Statistics, University of Chicago, US

Two volume set 1995 800 pp Hardback 978 1 85278 668 7

Hardback £252.00 on-line price £226.80

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Series: The International Library of Critical Writings in Econometrics series






Description
This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian econometrics.

Contents
30 articles, dating from 1926 to 1990 Contents: Volume I: Introduction 1. Foundations 2. Information Theory and Prior Distributions 3. Robustness and Outlier Index • Volume II: 1. Hierarchical, Multivariate and Non-Parametric Models 2. Asymptotics 3. Computation via Monte Carlo Methods 4. Bayesian Econometrics Index Contributors include: J.O. Berger, G.E.P. Box, T.S. Ferguson, D.V. Lindley, L.J. Savage, M.A. Tanner, A. Zellner

Further information

This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness and outliers; hierarchical, multivariate and non-parametric models; asymptotics; computations and Monte Carlo methods; and Bayesian econometrics.



 
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