Monte Carlo without Chains
Numerical Analysis
2008-02-09 v1 Disordered Systems and Neural Networks
Computational Physics
Abstract
A sampling method for spin systems is presented. The spin lattice is written as the union of a nested sequence of sublattices, all but the last with conditionally independent spins, which are sampled in succession using their marginals. The marginals are computed concurrently by a fast algorithm; errors in the evaluation of the marginals are offset by weights. There are no Markov chains and each sample is independent of the previous ones; the cost of a sample is proportional to the number of spins (but the number of samples needed for good statistics may grow with array size). The examples include the Edwards-Anderson spin glass in three dimensions.
Keywords
Cite
@article{arxiv.0802.1046,
title = {Monte Carlo without Chains},
author = {Alexandre Chorin},
journal= {arXiv preprint arXiv:0802.1046},
year = {2008}
}
Comments
17 pages; 2 figures