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In this paper we study simulation-based methods for estimating gradients instochastic networks. We derive a new method of calculating weak derivativeestimator using importance sampling transform, and our method has lesscomputational cost than the classical method. In the context of M/M/1 queueingnetwork and stochastic activity network, we analytically show that our newmethod won't result in a great increase of sample variance of the estimators.Our numerical experiments show that under same simulation time, the new methodcan yield a narrower confidence interval of the true gradient than theclassical one, suggesting that the new method is more competitive.

Using Importance Samping in Estimating Weak Derivative