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 language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Jul 3 2017|
Bibliographical noteFunding Information:
D.F. acknowledges support by Engineering and Physical Sciences Research Council (EPSRC) Program Grant EP/I001352/1 and EPSRC Grant EP/I000844/1. K.J.S. acknowledges support by the Swiss National Science Foundation (Grants P2EZP2-152188 and P300P2-161078). S.M. acknowledges financial support from the Gates Cambridge Scholarship.
© 2017, National Academy of Sciences. All rights reserved.
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