This book presents some of Arnold Zellner's outstanding contributions to the philosophy, theory and application of Bayesian analysis, particularly as it relates to statistics, econometrics and economics.

The volume contains both previously published and new material which cite and discuss the work of Bayesians who have made a contribution by helping researchers and analysts in many professions to become more effective in learning from data and making decisions. Bayesian and non-Bayesian approaches are compared in several papers. Other articles include theoretical and applied results on estimation, model comparison, prediction, forecasting, prior densities, model formulation and hypothesis testing. In addition, a new information processing approach is presented that yields Bayes's Theorem as a perfectly efficient information processing rule.

This volume will be essential reading for academics and students interested in qualitative methods as well as industrial analysts and government officials.

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The late Arnold Zellner, formerly HGB Alexander Distinguished Service Professor of Economics and Statistics, Emeritus, University of Chicago, US

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