Decision Analysis: A Bayesian Approach

This text provides an introduction to Bayesian decision analysis. It provides, for both the student and graduate of mathematical sciences, the tools by which to express and then solve the difficult decision problems that must be tackled in a business environment. The only prerequisite for using the book is an introductory course in probability and statistics such as is given to students of mathematics engineering, the physical sciences or economics. In order to emphasize the problem-solving aspects of decision analysis, the mathematical content has been kept to a minimum. On the other hand, however, there are occasions when the decision analyst must understand the underlying mathematics, as when selecting the timely use of an appropriate problem solving technique. In such cases the full mathematical argument has been included. The book begins with an introduction to the rudiments of decision analysis and moves on to decision trees, utilities and rewards. Subjective probabilities and their measurement lead on to influence diagrams and group decisions. The final chapters are devoted to Bayesian statistics and Bayes estimation in decision analysis. There are well over 50 exercises and almost 30 worked examples to illustrate the various practical and computational techniques of Bayesian problem solving. References from current review literature are also included.