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Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation
2006
Operations Research
For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing step-size stochastic approximation. The updates are shown to concentrate asymptotically in a neighborhood of the desired zero variance estimator. Through simulation experiments on simple Markovian queues, we observe that the proposed
doi:10.1287/opre.1060.0291
fatcat:7tkmixibtfesvlmuxdtifutem4