Optimizing weighted ensemble sampling of steady states

David Aristoff, Daniel M. Zuckerman

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)646-673
Number of pages28
JournalMultiscale Modeling and Simulation
Issue number2
StatePublished - 2020


  • Coarse graining
  • Markov chains
  • Molecular dynamics
  • Reaction networks
  • Resampling
  • Sequential monte carlo
  • Steady state
  • Weighted ensemble

ASJC Scopus subject areas

  • General Chemistry
  • Modeling and Simulation
  • Ecological Modeling
  • General Physics and Astronomy
  • Computer Science Applications


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