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Decision Sciences Ph.D. Courses
Decision Sciences faculty regularly teach the following Ph.D. courses.
BA 510, Bayesian Inference and Decision. Methods of Bayesian inference and statistical decision theory, with emphasis on the general approach of modeling inferential and decision-making problems as well as the development of specific procedures for certain classes of problems. Topics include subjective probability, evaluation and assessment of probabilities, Bayesian inference and prediction, comparisons with classical methods, value of information, sequential decision making, and Bayesian game theory. (Taught by Bob Winkler, offered every Fall.)
BA 513, Choice Theory. This course deals with advanced topics in choice theory, with emphasis on rational choice and the interface between decision theory, game theory, theories of markets, and social choice theory. The goal is to acquaint students with historical developments as well as recent advances in choice theory and to equip students that can be used in a wide variety of social science applications. (Taught by Bob Nau, offered every other year.)
BA 591, Prescriptive Models for Decision Analysis. This course serves as an introduction to decision analysis, including methods and tools for structuring decision problems, eliciting and modeling subjective probabilities, and modeling risk attitudes and conflicting objectives using utility theory. The goal is to understand the research frontiers in the field and to identify promising opportunities for future research. (Taught by Bob Clemen.)
BA 591, Dynamic Programming and Optimal Control. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty. We introduce discrete and continues time models with finite and infinite planning horizon. Applications are drawn from economics, finance, operations and engineering. (Taught by Peng Sun, offered every Fall.)
BA 591, Convex Optimization. This course provides an in-depth treatment of convex optimization, with a particular emphasis on duality. Linear programming is covered in detail, as well as problems over more general cones. Motivating applications from finance, operations, engineering, and economics are explored. (Taught by David Brown, offered every other year.)
Other seminars may be offered depending on faculty and student interest. For a list of courses that we recommend for Decision Sciences Ph.D. students, see Decision Sciences Ph.D. Courses and Programs of Study.