Monte Carlo sampling for stochastic weight functions
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 has the potential to transform the methodology of a certain class of highthroughput experiments or the analysis of noisy datasets.
 Publication:

arXiv eprints
 Pub Date:
 December 2016
 arXiv:
 arXiv:1612.06131
 Bibcode:
 2016arXiv161206131F
 Keywords:

 Condensed Matter  Statistical Mechanics;
 Physics  Computational Physics;
 Statistics  Methodology;
 Statistics  Machine Learning
 EPrint:
 7 pages, 4 figures