Optimal Importance Sampling in Markov Process Simulation
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Abstract:
In this paper we present an adaptive algorithm to estimate the transient blocking probability of a communication system, described by a Markov process, during a finite time interval starting from a given state. The method uses importance sampling for variance reduction and adjusts the parameters of the twisted distribution based on earlier samples. The method can be effectively applied to a decision making problem where future revenues are estimated with extensive simulations, in order to find an improved policy by so called first policy iteration.
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