Optimizing weighted ensemble sampling of steady states

TL;DR

This paper introduces parameter optimization methods for weighted ensemble sampling of Markov chains in steady states, improving efficiency by optimizing bin choices and replica counts, demonstrated through numerical comparisons.

Contribution

It presents novel strategies for optimizing weighted ensemble parameters based on first principles, enhancing sampling efficiency in steady-state Markov chain simulations.

Findings

Optimized bin and replica configurations outperform traditional methods.

New strategies improve sampling efficiency in steady-state regimes.

Numerical examples validate the effectiveness of the proposed methods.

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.