Bayesian Decision Analysis: Principles and Practice

Bayesian Decision Analysis: Principles and Practice

by Jim Q. Smith
ISBN-10:
0521764548
ISBN-13:
9780521764544
Pub. Date:
09/23/2010
Publisher:
Cambridge University Press
ISBN-10:
0521764548
ISBN-13:
9780521764544
Pub. Date:
09/23/2010
Publisher:
Cambridge University Press
Bayesian Decision Analysis: Principles and Practice

Bayesian Decision Analysis: Principles and Practice

by Jim Q. Smith

Hardcover

$79.0
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Overview

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.

Product Details

ISBN-13: 9780521764544
Publisher: Cambridge University Press
Publication date: 09/23/2010
Edition description: New Edition
Pages: 348
Product dimensions: 7.09(w) x 10.04(h) x 0.83(d)

About the Author

Jim Q. Smith is a Professor of Statistics at the University of Warwick.

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.
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