Bayesian Decision Analysis

Principles and Practice

Jim Q. Smith (University of Warwick)

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English

Cambridge University Press
23 September 2010

Mathematics & Sciences; Probability & statistics

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.

By: Jim Q. Smith (University of Warwick)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 255mm, Width: 180mm, Spine: 21mm
Weight: 840g
ISBN: 9780521764544
ISBN 10: 0521764548
Publication Date: 23 September 2010
Audience: College/higher education , A / AS level
Format: Hardback
Publisher's Status: Active

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.

Reviews for Bayesian Decision Analysis: Principles and Practice

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