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

Game theory isn't used all that much for adaptive clinical trials except in the form of statistical decision theory. Statistical decision theory and Bayesian analysis. For an overview of decision theory from this literature (Pearl 2000, ch. The basics of probability theory; 10.2. Bayes theorem for updating probabilities; 10.3. A list of references on Bayesian Title: Statistical Decision Theory and Bayesian Analysis Author: James O. While an innocuous theory, practical use of the Bayesian approach requires consideration of complex practical issues, including the source of the prior distribution, the choice of a likelihood function, computation and summary of the posterior . However, this may be impractical, particularly when the posterior is high-dimensional. Now we return to an analysis of decision scenarios, armed with EDT and the counterfactual formulation of CDT. This discussion also reminds me of some statistical debates, where many statisticians have argued the need for a decision-theoretic approach to analysis. 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. 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. For inference, a full report of the posterior distribution is the correct and final conclusion of a statistical analysis.