Monte Carlo sampling for stochastic weight functions
Proceedings of the National Academy of Sciences of the United States of America
National Academy of Sciences
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Frenkel, D., Schrenk, K., & Martiniani, S. (2017). Monte Carlo sampling for stochastic weight functions. Proceedings of the National Academy of Sciences of the United States of America, 114 (27), 6924-6929. https://doi.org/10.1073/pnas.1620497114
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.
Monte Carlo simulations, basin volumes, free-energy calculation, stochastic optimization, transition state
D. F. acknowledges support by EPSRC Programme Grant EP/I001352/1 and EPSRC grant EP/I000844/1. K. J. S. acknowledges support by the Swiss National Science Foundation (Grant No. P2EZP2-152188 and No. P300P2-161078). S. M. acknowledges financial support from the Gates Cambridge Scholarship.
External DOI: https://doi.org/10.1073/pnas.1620497114
This record's URL: https://www.repository.cam.ac.uk/handle/1810/266667