Abstract
Much of the rare-event simulation literature is concerned with the development of asymptotically optimal algorithms. Because of the difficulties associated with applying these ideas to complex models, this paper focuses on sub-optimal procedures that can be shown to be much more efficient than conventional crude Monte Carlo. We provide two such examples, one based on ‘‘repeated acceptance/rejection’’ as a means of computing tail probabilities for hitting time random variables and the other based on filtered conditional Monte Carlo.
Xiaowei Zhang
Associate Professor
My research research focuses on methodological advances in stochastic simulation and optimization, decision analytics, and reinforcement learning, with applications in service operations management, financial technology, and digital economy.