论文标题
利用重要性来估计弱衍生物
Using Importance Samping in Estimating Weak Derivative
论文作者
论文摘要
在本文中，我们研究了基于仿真的方法，用于估计随机网络中的梯度。我们得出了一种使用重要性采样变换来计算弱衍生物估计量的新方法，而我们的方法的计算成本少于经典方法。在M/M/1排队网络和随机活动网络的背景下，我们分析表明，我们的新方法不会大大增加估计器的样本差异。我们的数值实验表明，在相同的模拟时间下，新方法比经典梯度的置信区间更窄，这表明新方法更具竞争力。
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.
