Monte Carlo sampling for stochastic weight functions

Daan Frenkel, K. Julian Schrenk, Stefano Martiniani

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here, we consider the case that the weight determining the acceptance probability itself is fluctuating. This situation is common in many numerical studies. We show that it is possible to construct a rigorous Monte Carlo algorithm that visits points in state space with a probability proportional to their average weight. The same approach may have applications for certain classes of high-throughput experiments and the analysis of noisy datasets.

Original languageEnglish (US)
Pages (from-to)6924-6929
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number27
DOIs
StatePublished - Jul 3 2017
Externally publishedYes

Keywords

  • Basin volumes
  • Free-energy calculation
  • Monte Carlo simulations
  • Stochastic optimization
  • Transition state

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