Bayesian decision analysis : principles and practice / Jim Q. Smith.
By: Smith, J. Q.
Material type: BookPublisher: Cambridge, UK ; New York : Cambridge University Press, 2010Description: ix, 338 p. : ill. ; 26 cm.ISBN: 9780521764544; 0521764548.Subject(s): Bayesian statistical decision theoryDDC classification: 519.542 Online resources: Cover image | Table of contents only | Publisher description | Contributor biographical informationItem type | Current location | Call number | Copy number | Status | Date due |
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Monograph | Indian Institute of Management Udaipur B2/3 | 519.542 (Browse shelf) | 1 | Available |
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519.54068 Data Analysis in Business Research : a step-by-step nonparametric approach | 519.542 Rational Decisions | 519.542 Bayesian Methods in Health Economics | 519.542 Bayesian decision analysis : | 519.542 Bayesian Modeling of Spatio-Temporal Data with R / | 519.542620 Bayesian reliability / | 519.544 Guesstimation : |
Includes bibliographical references and index.
Machine generated contents note: Preface; Part I. Foundations of Decision Modeling: 1. Introduction; 2. Explanations of processes and trees; 3. Utilities and rewards; 4. Subjective probability and its elicitation; 5. Bayesian inference for decision analysis; Part II. Multi-Dimensional Decision Modeling: 6. Multiattribute utility theory; 7. Bayesian networks; 8. Graphs, decisions and causality; 9. Multidimensional learning; 10. Conclusions; Bibliography.
"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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