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|a Bayesian decision analysis :
|b principles and practice /
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|a "Bayesian 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"--Provided by publisher
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|a Includes bibliographical references (pages 322-334) and index.
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0 |
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|a Foundations of decision modeling. Introduction -- Explanations of processes and trees -- Utilities and rewards -- Subjective probability and its elicitation -- Bayesian inference for decision analysis Multi-dimensional decision modeling. Multiattribute utility theory -- Bayesian networks -- Graphs, decisions and causality -- Multidimensional learning -- Conclusions.
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|a Bayesian statistical decision theory.
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|i Print version:
|a Smith, J.Q., 1953-
|t Bayesian decision analysis.
|d Cambridge, UK ; New York : Cambridge University Press, 2010
|z 9780521764544
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