Bayesian Decision Analysis
Principles and Practice

A textbook and guide to conducting Bayesian decision analysis of sometimes very complex policies and collaborative decisions.

Jim Q. Smith (Author)

9780521764544, Cambridge University Press

Hardback, published 23 September 2010

348 pages, 65 exercises
25.5 x 18 x 2.1 cm, 0.84 kg

'… an excellent resource for students at final year undergraduate level or higher, and for anyone researching issues of complex decision-making.' Mathematics Today

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.

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.

Subject Areas: Artificial intelligence [UYQ], Probability & statistics [PBT], Management decision making [KJMD], Decision theory: general [GPQ]