Nicholas Guttenberg, Aaron R. Dinner, Jonathan Weare
We introduce a path sampling method for obtaining statistical properties of
an arbitrary stochastic dynamics. The method works by decomposing a trajectory
in time, estimating the probability of satisfying a progress constraint,
modifying the dynamics based on that probability, and then reweighting to
calculate averages. Because the progress constraint can be formulated in terms
of occurrences of events within time intervals, the method is particularly well
suited for controlling the sampling of currents of dynamic events. We
demonstrate the method for calculating transition probabilities in barrier
crossing problems and survival probabilities in strongly diffusive systems with
absorbing states, which are difficult to treat by shooting. We discuss the
relation of the algorithm to other methods.
View original:
http://arxiv.org/abs/1202.0316
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