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.Read more
- 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
Reviews & endorsements
'The author presents a good set of solved exercises, which serve for illustration, and a large set of proposed exercises are suggested. I recommend this book for professional and advanced students in statistics, operations research, computer science, artificial intelligence, cognitive sciences and different branches of engineering.' Narciso Bouza Herrera, Zentralblatt MATHSee more reviews
'… an excellent resource for students at final year undergraduate level or higher, and for anyone researching issues of complex decision-making.' Mathematics Today
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- Date Published: September 2010
- format: Hardback
- isbn: 9780521764544
- length: 348 pages
- dimensions: 255 x 180 x 21 mm
- weight: 0.84kg
- contains: 65 exercises
- availability: Available
Table of Contents
Part I. Foundations of Decision Modeling:
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
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