A Bayesian approach to feasibility determination

We propose a computing budget allocation scheme for feasibility determination in a stochastic setting. More formally, we propose a Bayesian approach to determine whether a system belongs to a given set based on performance measures estimated through Monte Carlo simulation. We introduce two adaptive approaches in the sense that the computational budget is allocated dynamically based on the samples obtained thus far. The first approach determines the number of additional samples required so that the posterior probability that a system's mean performance is correctly classified is at least 1-δ in expectation, while the second approach determines the number of additional samples so that the posterior probability that the system mean lies inside or outside of the feasible region is at least 1-δ with a desired probability. Preliminary numerical experiments are reported.