Ensemble preconditioning for Markov chain Monte Carlo simulation
Methodology
2016-07-15 v1 Numerical Analysis
Computation
Machine Learning
Abstract
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable.
Cite
@article{arxiv.1607.03954,
title = {Ensemble preconditioning for Markov chain Monte Carlo simulation},
author = {Charles Matthews and Jonathan Weare and Benedict Leimkuhler},
journal= {arXiv preprint arXiv:1607.03954},
year = {2016}
}