Monte Carlo sampling for stochastic weight functions.
basic_science · Level V
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- Record sourced from PubMed, PMID 28634292.
- Also identified by DOI 10.1073/pnas.1620497114 and PMC identifier 5502596.
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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.