CAREER: Markov Chain Monte Carlo Methods

职业：马尔可夫链蒙特卡罗方法

• 批准号: 0455666
• 负责人: Eric Vigoda
• 金额: $ 29.75万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0455666&HistoricalAwards=false
• 关键词: CAREER; Markov; Chain; Monte; Carlo

Markov chain Monte Carlo (MCMC) methods are an importantalgorithmic device in a variety of fields. This project studiestechniques for rigorous analysis of the convergence properties ofMarkov chains. The emphasis is on refining probabilistic,analytic and combinatorial tools (such as coupling, log-Sobolev,and canonical paths) to improve existing algorithms and developefficient algorithms for important open problems.Problems arising in computer science, discrete mathematics,and physics are of particular interest, e.g., generating randomcolorings and independent sets of bounded-degree graphs,approximating the permanent, estimating the volume of aconvex body, and sampling contingency tables. The projectalso studies inherent connections between phasetransitions in statistical physics models and convergenceproperties of associated Markov chains.The investigator is developing a new graduate course on MCMC methods.

马尔可夫链蒙特卡洛（MCMC）方法是各种领域中的重要载体设备。 该项目studiestechniques用于对Markov链的收敛性能进行严格分析。 重点是完善概率，分析和组合工具（例如耦合，log-sobolev和canonical路径），以改善重要的开放问题的现有算法和Developefffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffs oblesim and Intrimim groping grop and Indifsition in tocrors of Insports，例如，分别是独立的，例如永久估计Aconvex主体的体积和采样应急表。 该概况研究了统计物理模型和关联的马尔可夫链的汇聚术之间的固有联系。研究人员正在开发有关MCMC方法的新研究生课程。