Using Importance Samping in Estimating Weak Derivative
Methodology
2023-03-28 v3 Optimization and Control
Abstract
In this paper we study simulation-based methods for estimating gradients in stochastic networks. We derive a new method of calculating weak derivative estimator using importance sampling transform, and our method has less computational cost than the classical method. In the context of M/M/1 queueing network and stochastic activity network, we analytically show that our new method won't result in a great increase of sample variance of the estimators. Our numerical experiments show that under same simulation time, the new method can yield a narrower confidence interval of the true gradient than the classical one, suggesting that the new method is more competitive.
Cite
@article{arxiv.2209.13184,
title = {Using Importance Samping in Estimating Weak Derivative},
author = {Cheng Jie and Michael C Fu},
journal= {arXiv preprint arXiv:2209.13184},
year = {2023}
}