Improving the efficiency of Markov chain Monte Carlo

提高马尔可夫链蒙特卡罗的效率

• 批准号: 293260-2007
• 负责人: Yuen, WaiKong
• 金额: $ 1.17万
• 依托单位: Brock University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=480549
• 关键词: Improving; efficiency; Markov; chain; Monte

Markov chain Monte Carlo (MCMC) algorithms are widely used in the scientific community. They are used to generate approximate samples from complicated target distributions resulting from statistical and applied probability models. In particular, generic algorithms such as Metropolis and Langevin algorithms are very popular because of the ease of implementations. However, it is well known that choosing the best proposal distribution is not an easy task. Failing to do that can result in an inefficient algorithm and misleading results. My research program will focus on the theory and implementation of MCMC algorithms, typically on unbounded Euclidean state spaces, motivated by distributions such as those encountered in Bayesian inference. The three main objectives of my proposed research program are:

马尔可夫链蒙特卡罗（MCMC）算法在科学界有着广泛的应用。它们用于从统计和应用概率模型产生的复杂目标分布中生成近似样本。特别是，通用算法，如大都会和朗之万算法是非常流行的，因为易于实现。然而，众所周知，选择最佳建议分布并不是一件容易的事情。如果不这样做，可能会导致效率低下的算法和误导性的结果。我的研究计划将侧重于MCMC算法的理论和实现，通常在无界欧几里得状态空间上，由贝叶斯推理中遇到的分布驱动。我提出的研究计划的三个主要目标是：