The "weighted ensemble" path sampling method is statistically exact for a broad class of stochastic processes and binning procedures.
The "weighted ensemble" method, introduced by Huber and Kim [Biophys. J. 70, 97 (1996)], is one of a handful of rigorous approaches to path sampling of rare events. Expanding earlier discussions, we show that the technique is statistically exact for a wide class of Markovian and non-Markovian dynamics. The derivation is based on standard path-integral (path probability) ideas, but recasts the weighted-ensemble approach as simple "resampling" in path space. Similar reasoning indicates that arbitrary nonstatic binning procedures, which merely guide the resampling process, are also valid. Numerical examples confirm the claims, including the use of bins which can adaptively find the target state in a simple model.
Zhang
Bin W
BW
Department of Computational Biology, School of Medicine, University of Pittsburgh, Pennsylvania 15260, USA.
Jasnow
David
D
Zuckerman
Daniel M
DM
D019295:Computational Biology; D008954:Models, Biological; D008956:Models, Chemical; D011485:Protein Binding / Q000235:genetics; D017510:Protein Folding; D013269:Stochastic Processes