Bayesian decision analysis : principles and practice
Cambridge, UK
Cambridge University Press
2010
ix, 338 p. : ill.
Includes bibliographical references )p. 322-334( and index
Jim Q. Smith
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; 01. Conclusions; Bibliography