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.

翻译：本文研究了基于仿真的方法以估计随机网络中的梯度。我们提出了一种利用重要性抽样变换计算弱导数估计量的新方法，该方法相较于经典方法具有更低计算成本。在M/M/1排队网络与随机活动网络的背景下，我们通过解析分析表明新方法不会导致估计量样本方差的显著增大。数值实验结果显示，在相同仿真时长下，新方法能生成比经典方法更窄的真实梯度置信区间，这表明新方法更具竞争力。