Evolutionary and energy-domain Monte Carlo algorithms and their applications

演化和能量域蒙特卡罗算法及其应用

• 批准号: 0505732
• 负责人: Wing Hung Wong
• 金额: --
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0505732&HistoricalAwards=false
• 关键词: Evolutionary; energy; domain; Monte; Carlo

Abstract for DMS - 0505732PI: Professor Wing Hung WongINSTITUTION: STANFORD UNIVERSITYTITLE: Evolution and Energy Domain Monte Carlo algorithms and their applicationsThis project will develop two new algorithms for Monte Carlo simulation and applied them to several problems in science and technology. The first method, called Evolutionary Monte Carlo, was introduced by the PI's laboratory under prior NSF support. It has been applied with excellent results in challenging problems such as the HP model in protein folding and Cp-based model selection. Here, it is proposed that Evolutionary MC be developed for the computational inference of network structure for directed acylic graphical models (DAG). The second method, called equi-energy sampling, is in an early stage of development by the PI and his collaborators. The idea is to generate samples from the equi-energy rings each of them having the energy lying within a restricted interval of values. An energy-temperature duality is exploited to allow the estimation of a Boltzmann average (i.e. averages corresponding to a fixed temperature) from estimates of the micro-canonical averages (i.e. averages within equi-energy rings). In addition to giving estimates of Boltzmann averages, this approach also provide estimates for the "density of states" function and the partition function. Thus equi-energy sampling can provide information for all thermodynamic quantities in a single run, and for this reason it should be a very attractive algorithm in physical applications such as protein folding. Recent development of Markov Chain Monte Carlo methods has allowed the application of statistical modeling and inference to more and more application areas. For many important applications such as DNA motif sampling, protein folding, and statistical inference of causal network structures, Monte Carlo computation has become an indispensable tool. The work proposed in this project will result in significant improvement of the performance Monte Carlo algorithms in problems with complex energy landscapes, and therefore will make it feasible to apply this method to a wider spectrum of problems.

摘要：DMS - 0505732PI：教授翁永雄机构：斯坦福大学标题：进化和能量域蒙特卡罗算法及其应用本项目将开发两种新的蒙特卡罗模拟算法，并将其应用于科学技术中的几个问题。第一种方法被称为进化蒙特卡罗，是由PI实验室在NSF的支持下引入的。它在蛋白质折叠中的HP模型和基于cp的模型选择等具有挑战性的问题中得到了很好的应用。在此，我们提出将进化MC发展为有向无环图模型（DAG）网络结构的计算推理。第二种方法被称为等能采样，PI和他的合作者还处于早期开发阶段。这个想法是从等能环中产生样本，每个环的能量都在一个有限的值区间内。利用能量-温度对偶性，可以从微规范平均（即等能环内的平均）的估计中估计玻尔兹曼平均（即对应于固定温度的平均）。除了给出玻尔兹曼平均的估计外，这种方法还提供了“态密度”函数和配分函数的估计。因此，等能采样可以在一次运行中提供所有热力学量的信息，因此，它应该是一个非常有吸引力的物理应用算法，如蛋白质折叠。马尔可夫链蒙特卡罗方法的最新发展使得统计建模和推理在越来越多的应用领域得到了应用。对于DNA基序采样、蛋白质折叠和因果网络结构的统计推断等许多重要应用，蒙特卡罗计算已经成为不可或缺的工具。本项目提出的工作将显著提高蒙特卡罗算法在复杂能量景观问题中的性能，因此将使该方法应用于更广泛的问题成为可能。