所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Statistics and computing
项目来源：国家自然科学基金项目
关键字：Gaussian graphical model · HT procedure · Iterative proportional scaling · Junction tree · Sharing computations
摘要：In this paper, we propose an improved iterative proportional scaling procedure for maximum likelihood estimation for multivariate Gaussian graphical models. Our proposed procedure allows us to share computations when adjusting different clique marginals on junction trees. This makes our procedure more efficient than existing procedures for maximum likelihood estimation for multivariate Gaussian graphical models. Some numerical experiments are conducted to illustrate the efficiency of our proposed procedure for maximum likelihood estimation of Gaussian graphical models with the number of variables up to the two thousands.We also demonstrate our proposed procedures by two genetic examples.
第一作者：徐平峰
论文类型：期刊论文
页面范围：1
是否译文：否
发表时间：2012-09-01