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.
- Numerous examples from a variety of real-world applications show how theory improves practice
- Includes new material not currently available in other teaching texts
- Designed especially for decision analysts who interact with stakeholders
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.
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