Bayesian Decision Analysis
Bayesian Decision Analysis
CAD$85.95
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
Reviews & endorsements
"[L]et me stress that the design and the printing of the book are both of the highest quality, numerous tree graphs appearing seamlessly at the right place [making captions superfluous], different fonts making parts more coherent and so on. I thus hope it is obvious I strongly recommend reading the book to all involved in any level of decision management! Or teaching it."
Xi'an's Og Blog
"The preface explains that the book is intended as a course resource for mathematically sophisticated undergraduates and students in a statistics master's program. It would serve this purpose admirably and would be a very good reference book for all researchers in this field."
R. Bharath, emeritus, Northern Michigan University for Choice Magazine
Product details
November 2010Hardback
9780521764544
348 pages
255 × 180 × 21 mm
0.84kg
65 exercises
Available
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.
