Statistical mechanics of histories: a cluster Monte Carlo algorithm.
We present an efficient computational approach to sample the histories of nonlinear stochastic processes. This framework builds upon recent work on casting a d-dimensional stochastic dynamical system into a (d+1)-dimensional equilibrium system using the path-integral approach. We introduce a cluster algorithm that efficiently samples histories and discuss how to include measurements that are available into the estimate of the histories. This allows our approach to be applicable to the simulation of rare events and to optimal state and parameter estimation. We demonstrate the utility of this approach for Phi4 Langevin dynamics in two spatial dimensions where our algorithm improves sampling efficiency up to an order of magnitude.
Gulbahce
Natali
N
Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545, USA.
Alexander
Francis J
FJ
Johnson
Gregory
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