状态相关的重要性抽样偏向方法在加权随机模拟算法中的应用。
State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm.
机构信息
Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA.
出版信息
J Chem Phys. 2010 Nov 7;133(17):174106. doi: 10.1063/1.3493460.
Abstract
The weighted stochastic simulation algorithm (wSSA) was developed by Kuwahara and Mura [J. Chem. Phys. 129, 165101 (2008)] to efficiently estimate the probabilities of rare events in discrete stochastic systems. The wSSA uses importance sampling to enhance the statistical accuracy in the estimation of the probability of the rare event. The original algorithm biases the reaction selection step with a fixed importance sampling parameter. In this paper, we introduce a novel method where the biasing parameter is state-dependent. The new method features improved accuracy, efficiency, and robustness.
摘要
加权随机抽样算法（wSSA）是由 Kuwahara 和 Mura [J. Chem. Phys. 129, 165101 (2008)]开发的，用于有效地估计离散随机系统中罕见事件的概率。wSSA 使用重要性抽样来提高稀有事件概率估计的统计准确性。原始算法通过固定的重要性抽样参数对反应选择步骤进行偏差。在本文中，我们引入了一种新的方法，其中偏差参数是状态相关的。新方法具有更高的准确性、效率和鲁棒性。
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