Peter Frazier - Cornell University (Joint Seminar with CISE)

Title: Bayesian Methods for Simulation Optimization. Abstract: We consider simulation optimization, in which we wish to solve an optimization problem whose objective function can only be evaluated using stochastic simulation. When the simulator is large and time-consuming, the time to solve a simulation optimization problem is gated by the number of simulation replications required. One increasingly popular approach to algorithm development for such problems is to place a Bayesian prior distribution on the underlying objective function, and to value potential function evaluations, or collections of function evaluations, according to the probability distribution of the improvement they would provide. We provide an overview of this class of algorithms, discussing links to decision theory and Markov decision processes, and present an application to the design of cardiovascular bypass grafts.