## Statistical decision theory and Bayesian analysis by James O. Berger Download PDF EPUB FB2

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The prerequisite is rather low. I Statistical level: moderately serious statistics I Mathematical level: easy advanced calculus.

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Reinhardt. ‘Bayesian Methods for Statistical Analysis’ is a book which can be used as the text for a semester-long course and is suitable for anyone who is familiar with statistics at the level of Mathematical Statistics with ‘ Applications’ by Wackerly, Mendenhall and Scheaffer ().

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Berger 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.

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) : Springer New York.

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/5(29).

This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers havinga basic familiarity with probability. About this Item: Springer-Verlag New York Inc.

Condition: New. 2nd. Hardcover. An introduction to Bayesian analysis. It contains material on Bayesian analysis, including sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making.

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Abstract. Decision theory is the science of making optimal decisions in the face of uncertainty. Statistical decision theory is concerned with the making of decisions when in the presence of statistical knowledge (data) which sheds light on some of the uncertainties involved in the decision.

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Books 1. Albert, J. (), Bayesian Computations with R, Springer, New York [Very basic introduction. Has some R2WinBUGS examples.] 2. Anderson, T. W. (), An Introduction to Mul-tivariate Analysis, Wiley, New York. [There.