所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Journal of Computational and Graphical Statistics
项目来源：国家自然科学基金项目
关键字：Junction tree; Probability propagation algorithm
摘要：The maximum likelihood estimation of Gaussian graphical models is often carried out by the iterative proportional scaling (IPS) procedure. In this article, we propose an improvement to the IPS procedure by using local computation and by sharing computations on a junction tree T . The proposed procedure, called IIPS for short, adjusts node by node the marginals of the cliques of the underlying graph contained in the nodes of T , and sends messages between two adjacent nodes of T by an exchange operation for the propositional scaling step. We show, through complexity calculations and empirical examples, that the proposed IIPS procedure works more efficiently than the conventional IPS procedure for large Gaussian graphical models. Computer codes used in this article are available as an online supplement.
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：2011-06-01