The Valencia International Meetings on Bayesian Statistics - established in 1979 and held every four years - have been the forum for a definitive overview of current concerns and activities in Bayesian statistics. These are the edited Proceedings of the Ninth meeting, and contain the invited papers each followed by their discussion and a rejoinder by the authors(s). In the tradition of the earlier editions, this encompasses an enormous range of theoretical and applied research, high lighting the breadth, vitality and impact of Bayesian thinking in interdisciplinary research across many fields as well as the corresponding growth and vitality of core theory and methodology. The Valencia 9 invited papers cover a broad range of topics, including foundational and core theoretical issues in statistics, the continued development of new and refined computational methods for complex Bayesian modelling, substantive applications of flexible Bayesian modelling, and new developments in the theory and methodology of graphical modelling. They also describe advances in methodology for specific applied fields, including financial econometrics and portfolio decision making, public policy applications for drug surveillance, studies in the physical and environmental sciences, astronomy and astrophysics, climate change studies, molecular biosciences, statistical genetics or stochastic dynamic networks in systems biology.

Bayesian Statistics 9

Science & Math

West, Mike

Smith, Adrian F. M.

Berger, James O.

Bernardo, José M.

Bayarri, M. J.

Dawid, A. P.

Heckerman, David

Mathematics

Oxford University Press

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics)

Springer

The likelihood principle (Lecture notes-monograph series)

Institute of Mathematical Statistics

The Likelihood Principle (Ims Lecture Notes-Monograph : Vol 6)

Wolpert, Robert L.

Inst of Mathematical Statistic

An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory. The text assumes a knowledge of basic probability theory and some advanced calculus is also required.

Reference

Foreign Language Study & Reference

Springer, Verlag

Objective Bayesian Inference

Bernardo, Jose-Miguel

Sun, Dongchu

World Scientific Pub Co Inc

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