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.
A textbook and guide to conducting Bayesian decision analysis of sometimes very complex policies and collaborative decisions.
Jim Q. Smith is a Professor of Statistics at the University of Warwick.
'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
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