Bayesian decision analysis : principles and practice
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Bibliographic Information
Bayesian decision analysis : principles and practice
Cambridge University Press, 2010
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Includes bibliographical references (p. 322-334) and index
Description and Table of Contents
Description
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.
Table of Contents
- 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.
by "Nielsen BookData"