期刊
MULTISCALE MODELING & SIMULATION
卷 18, 期 2, 页码 646-673出版社
SIAM PUBLICATIONS
DOI: 10.1137/18M1212100
关键词
Markov chains; resampling; sequential Monte Carlo; weighted ensemble; molecular dynamics; reaction networks; steady state; coarse graining
资金
- National Science Foundation [NSF-DMS-1522398, NSF-DMS-1818726]
- National Institutes of Health [GM115805]
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.
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