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Statistical decision theory and bayesian analysis by James O. Berger

Statistical decision theory and bayesian analysis James O. Berger ebook
Format: djvu
Publisher: Springer
ISBN: 0387960988, 9780387960982
Page: 316

In a full Bayesian data analysis (with spatial data or not), a loss function should be specified that relates to the decision made from the results of the analysis – the loss function should capture the “consequences” of making a given decision. Numerical Analysis for Statisticians. Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. The use of Bayesian probabilities as the basis of Bayesian inference has been supported by several arguments, such as the Cox axioms, the Dutch book argument, arguments based on decision theory and de Finetti's theorem. In the objectivist stream, the statistical analysis depends on only the model assumed and the data analysed. In contrast, "subjectivist" statisticians deny the Justification of Bayesian probabilities. This discussion also reminds me of some statistical debates, where many statisticians have argued the need for a decision-theoretic approach to analysis. The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation. Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions. 154, Combinatorics graph theory algorithms and applications. 152, Statistical Decision theory and bayesian analysis. Statistical decision theory and Bayesian analysis. Statistical Decision Theory and Bayesian Analysis, 2nd Ed., Springer-Verlag. ȴ�叶斯: Statistical Decision Theory and Bayesian Analysis. Statistical Decision Theory and Bayesian Analysis. No subjective decisions need to be involved. 153, Applied and algorithmic graph.