Bayesian statistics 3: proceedings of the Third Valencia International Meeting, June 1-5, 1987
The field of statistics has undergone rapid and wide development during the past two decades, and the Bayesian approach to statistics has provided both a general framework and a creative stimulus for all aspects of this development. This volume describes the work presented at the Third Valencia International Meeting on Bayesian Statistics, the main source of information and communication about the current state of knowledge and research in Bayesian statistics throughout the world. The research presented--which encompasses both invited papers and selected contributed papers-- has had a profound effect on the foundations of statistical inference and probability, statistical theory and methodology, and the applications of statistics in science, technology, medicine, business, law, and public policy. The contributors to this volume form a virtual Who's Who in the area of Bayesian statistics.
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De Finettis approach
The future of statistics teaching and research
Gaining weight a Bayesian approach
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A. F. M. Smith algorithm applied approximation assessment assume assumption asymptotic Bayes estimates Bayesian analysis Bayesian approach Bayesian inference Bayesian Statistics belief calculations clinical trials coefficients components compute conditional conjugate prior consider covariance matrix D. V. Lindley data set decision defined denote Diaconis discussion Econometrics empirical Bayes error evaluated example exchangeable expected exponential family Figure Finetti finite follows given hierarchical hyperparameters hypothesis incarceration independent interval J. M. Bernardo Kadane likelihood function Lindley and A. F. M. linear model M. H. DeGroot marginal distribution methods multiple multiple comparisons multivariate nodes normal distribution observations obtained optimal paper parameters population posterior density posterior distribution posterior mean posterior probability predictive distribution prior distribution prior information prior probability problem procedure random variables regression regressors robustness sample Section sequence specification statisticians structure Suppose Table theorem transformation values variance vector weight Zellner zero