Efficient Sub-Optimal Rare-Event Simulation

X. Zhang, P. W. Glynn, and J. Blanchet

Proceedings of the 2007 Winter Simulation Conference, 389-394 (2007)

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 mean of computing tail probabilities for hitting time random variables and the other based on filtered conditional Monte Carlo.