An adaptive Metropolis-Hastings scheme: sampling and optimization
其他凝聚态物理
2007-05-23 v1 无序系统与神经网络
摘要
We propose an adaptive Metropolis-Hastings algorithm in which sampled data are used to update the proposal distribution. We use the samples found by the algorithm at a particular step to form the information-theoretically optimal mean-field approximation to the target distribution, and update the proposal distribution to be that approximatio. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm in sampling and in annealing optimization.
引用
@article{arxiv.cond-mat/0504163,
title = {An adaptive Metropolis-Hastings scheme: sampling and optimization},
author = {David H. Wolpert and Chiu Fan Lee},
journal= {arXiv preprint arXiv:cond-mat/0504163},
year = {2007}
}
备注
To appear in Europhysics Letters