OPTIMIZING WEIGHTED ENSEMBLE SAMPLING OF STEADY STATES.
MULTISCALE MODELING & SIMULATION : A SIAM INTERDISCIPLINARY JOURNAL 2020;
18:646-673. [PMID:
34421402 PMCID:
PMC8378190 DOI:
10.1137/18m1212100]
[Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
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.
Collapse