模拟生物分子的马尔可夫链蒙特卡罗方法
Markov-Chain Monte Carlo Methods for Simulations of Biomolecules
摘要：
自1950年以来，计算速度每五年大约增加一个数量级（十倍），推动了计算机革命。在自然科学中，这导致计算机模拟的重要性不断增加。主要的使能技术是马尔可夫链蒙特卡罗（MCMC）和分子动力学（MD）模拟。本文讨论MCMC方法。首先，基本的模拟技术，以及他们的统计分析方法进行审查。之后，重点是广义的合奏和偏见的更新，两个先进的技术，这是相关的生物分子的模拟，或预计将成为相关的这方面。特别是，我们考虑的multicanonical合奏和副本交换方法（也称为并行回火或方法的多个马尔可夫链）。
英文摘要：
The computer revolution has been driven by a sustained increase of computational speed of approximately one order of magnitude (a factor of ten) every five years since about 1950. In natural sciences this has led to a continuous increase of the importance of computer simulations. Major enabling techniques are Markov Chain Monte Carlo (MCMC) and Molecular Dynamics (MD) simulations. This article deals with the MCMC approach. First basic simulation techniques, as well as methods for their statistical analysis are reviewed. Afterwards the focus is on generalized ensembles and biased updating, two advanced techniques, which are of relevance for simulations of biomolecules, or are expected to become relevant with that respect. In particular we consider the multicanonical ensemble and the replica exchange method (also known as parallel tempering or method of multiple Markov chains).
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