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The Resource Bayesian decision analysis : principles and practice, Jim Q. Smith

Bayesian decision analysis : principles and practice, Jim Q. Smith

Label
Bayesian decision analysis : principles and practice
Title
Bayesian decision analysis
Title remainder
principles and practice
Statement of responsibility
Jim Q. Smith
Creator
Subject
Language
eng
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"--Provided by publisher
Cataloging source
DLC
Illustrations
illustrations
Index
index present
LC call number
QA279.5
LC item number
.S628 2010
Literary form
non fiction
Nature of contents
bibliography
Bayesian decision analysis : principles and practice, Jim Q. Smith
Label
Bayesian decision analysis : principles and practice, Jim Q. Smith
Publication
Related Contributor
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Bibliography note
Includes bibliographical references (p. 322-334) and index
Contents
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
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9780521764544&userID=ebsco-test&password=ebsco-test
Dimensions
26 cm.
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b3014007'}
Extent
ix, 338 p.
Isbn
9780521764544
Lccn
2010031690
Other physical details
ill.
System control number
(OCoLC)619125102

Library Locations

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      110 8th St, Troy, NY, 12180, US
      42.729766 -73.682577
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