Bayesian Statistical Inference and Decision Making

A study of the techniques of Bayesian statistical inference and decision making. The course is designed to introduce the student to the general concepts of the Bayesian approach - utilization of all available information. Specific topics will include probability - objective and subjective; discrete and continuous models; prior and posterior analysis; decision theory; utility and decision making; value of sample information; and pre-posterior analysis. Differences and similarities between classical and Bayesian analysis are discussed. All areas of decision making will be applied to business problems. Students interested in this course should see a department advisor.