A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Importance sampling for stochastic simulations
Management Science
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal importance sampling for quick simulation of highly reliable Markovian systems
WSC '93 Proceedings of the 25th conference on Winter simulation
Approximate zero-variance simulation
Proceedings of the 40th Conference on Winter Simulation
Hi-index | 0.00 |
In this paper we describe several computational algorithms useful in studying importance sampling (IS) for Markov chains. Our algorithms compute optimal IS measures and evaluate the estimate variance for a given measure. As knowledge of the optimal IS measure implies knowledge of the quantity to be estimated, our algorithms produce this quantity as a by-product. Since effective IS measures must often closely approximate the optimal measure, the use of these algorithms for small problems may produce in sights that lead to effective measures for larger problems of actual interest. We consider two classes of problems: hitting times and fixed-horizon costs.