Optimized Monte Carlo Methods
无序系统与神经网络
2008-02-03 v1 统计力学
高能物理 - 格点
摘要
I discuss optimized data analysis and Monte Carlo methods. Reweighting methods are discussed through examples, like Lee-Yang zeroes in the Ising model and the absence of deconfinement in QCD. I discuss reweighted data analysis and multi-hystogramming. I introduce Simulated Tempering, and as an example its application to the Random Field Ising Model. I illustrate Parallel Tempering, and discuss some technical crucial details like thermalization and volume scaling. I give a general perspective by discussing Umbrella Methods and the Multicanonical approach.
引用
@article{arxiv.cond-mat/9612010,
title = {Optimized Monte Carlo Methods},
author = {Enzo Marinari},
journal= {arXiv preprint arXiv:cond-mat/9612010},
year = {2008}
}
备注
Lectures given at the 1996 Budapest Summer School on Monte Carlo Methods. 35 pages including 17 figures