Bayesian decision analysis : principles and practice

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Title:

Bayesian decision analysis : principles and practice

Author:

Smith, J. Q., 1953-

ISBN:

9780521764544

Personal Author:

Smith, J. Q., 1953-

Publication Information:

Cambridge, UK ; New York : Cambridge University Press, 2010.

Physical Description:

ix, 338 p. : ill. ; 26 cm.

General Note:

Formerly CIP.

Contents:

Machine generated contents note: 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.

Abstract:

"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"--Provided by publisher.

Subject Term:

Bayesian statistical decision theory.

Electronic Access:

Publisher description http://www.loc.gov/catdir/enhancements/fy1011/2010031690-d.html
Table of contents only http://www.loc.gov/catdir/enhancements/fy1011/2010031690-t.html
Cover image http://assets.cambridge.org/97805217/64544/cover/9780521764544.jpg
Table of contents only http://catdir.loc.gov/catdir/enhancements/fy1011/2010031690-t.html
Publisher description http://catdir.loc.gov/catdir/enhancements/fy1011/2010031690-d.html
Contributor biographical information http://catdir.loc.gov/catdir/enhancements/fy1011/2010031690-b.html

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025498714	519.542 S651B 2010	1	Searching... Unknown

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Summary

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.