## Bayesian Decision Analysis: Principles and PracticeBayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics. |

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associated assume attributes auditor axiom Bayes decision Bayes rule Bayesian decision Bayesian inference Bayesian network calculate causal Chapter choose clique components conditional independence conditional independence statements conditional probabilities consequences d-separation d-separation theorem DAG G decision analysis decision problem decision rule decision tree deﬁned deﬁnition denote depend described difﬁcult Dirichlet discussed DM believes DM needs DM’s utility function edges event tree example expected payoff expected utility ﬁbre ﬁnd ﬁnite ﬁrst framework Furthermore gamble given graph hyperparameters identiﬁed inference judgements likelihood linear marginal likelihood mass function maximising measured Note observed optimal parameters particular possible posterior density posterior distribution predict preferences prior density probabilistic probability forecaster random variables reﬁned reﬂect reward sample satisﬁes scenarios score simple situations speciﬁcation structure subjective probability subtree Suppose suspect symptoms utility function vector vertex vertices whilst