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
Not yet reviewed
Be the first to review
Review was not posted due to profanity×
- Date Published: September 2010
- format: Hardback
- isbn: 9780521764544
- length: 348 pages
- dimensions: 255 x 180 x 21 mm
- weight: 0.82kg
- contains: 65 exercises
- availability: In stock
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
Find resources associated with this titleYour search for '' returned .
Type Name Unlocked * Format Size
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact firstname.lastname@example.org.
Instructors have used or reviewed this title for the following courses
- Knowledge Management
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email email@example.comRegister Sign in
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.Continue ×