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Statistical decision theory and bayesian analysis James O. Berger
Publisher: Springer

Note that those from the field of statistics who work on decision theory tend to talk about a "loss function," which is simply an inverse utility function. Peter de Blanc, I don't have an example, just a vague memory of reading about minimax-optimal decision rules in J. Bayes theorem for updating probabilities; 10.3. This discussion also reminds me of some statistical debates, where many statisticians have argued the need for a decision-theoretic approach to analysis. While reading Chapter 22 of your book, Bayesian Data Analysis (2nd ed.) – I came upon the section on the *Distinction between decision analysis and 'statistical decision theory'* (p. Numerical Analysis for Statisticians. The basics of probability theory; 10.2. Statistical Decision Theory and Bayesian Analysis. Van der Linde, “Bayesian Measures of Model Complexity and Fit,” J. Statistical decision theory and bayesian analysis : PDF eBook Download. Berger, Statistical Decision Theory and Bayesian Analysis, second ed. Statistical decision theory and Bayesian analysis. Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics) by James O. Now we return to an analysis of decision scenarios, armed with EDT and the counterfactual formulation of CDT. 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. For an overview of decision theory from this literature (Pearl 2000, ch. October 21st, 2012 reviewer Leave a comment Go to comments. Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics) (Hardcover)by James O. Berger's Statistical Decision Theory and Bayesian Analysis.