Optimizing weighted ensemble sampling of steady states

Abstract

We propose parameter optimization techniques for weighted ensemble sampling of Markov chains in the steady-state regime. Weighted ensemble consists of replicas of a Markov chain, each carrying a weight, that are periodically resampled according to their weights inside of each of a number of bins that partition state space. We derive, from first principles, strategies for optimizing the choices of weighted ensemble parameters, in particular the choice of bins and the number of replicas to maintain in each bin. In a simple numerical example, we compare our new strategies with more traditional ones and with direct Monte Carlo.

28 pages, 5 figures

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Cited by in corpus (6)

• Weighted ensemble: Recent mathematical developments
• An ergodic theorem for the weighted ensemble method
• Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory
• Transient probability currents provide upper and lower bounds on non-equilibrium steady-state currents in the Smoluchowski picture
• Optimal input potential functions in the interacting particle system method
• A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications