Simple Monte Carlo and the Metropolis Algorithm
数值分析
2007-06-13 v2 概率论
摘要
We study the integration of functions with respect to an unknown density. We compare the simple Monte Carlo method (which is almost optimal for a certain large class of inputs) and compare it with the Metropolis algorithm (based on a suitable ball walk). Using MCMC we prove (for certain classes of inputs) that adaptive methods are much better than nonadaptive ones. Actually, the curse of dimension (for nonadaptive methods) can be broken by adaption.
引用
@article{arxiv.math/0611285,
title = {Simple Monte Carlo and the Metropolis Algorithm},
author = {Peter Mathe and Erich Novak},
journal= {arXiv preprint arXiv:math/0611285},
year = {2007}
}
备注
Journal of Complexity, to appear