State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm.
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.
Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA. min.roh@gmail.com
Min K
MK
Roh
Dan T
DT
Gillespie
Linda R
LR
Petzold
D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D008956:Models, Chemical; D011336:Probability; D012107:Research Design; D015983:Selection Bias; D013269:Stochastic Processes