An improved Hara-Takamura procedure by sharing computations on junction tree in Gaussian graphical models

被引：5
|
作者：
Xu, Ping-Feng [2 ,3 ]
Guo, Jianhua [1 ,2 ]
Tang, Man-Lai [4 ]
机构：
[1] NE Normal Univ, Key Lab Appl Stat MOE, Changchun 130024, Jilin Province, Peoples R China
[2] NE Normal Univ, Sch Math & Stat, Changchun 130024, Jilin Province, Peoples R China
[3] Changchun Univ Technol, Sch Basic Sci, Changchun 130012, Jlin Province, Peoples R China
[4] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
来源：
STATISTICS AND COMPUTING | 2012年 / 22卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Gaussian graphical model; HT procedure; Iterative proportional scaling; Junction tree; Sharing computations; GENE NETWORK; IMPLEMENTATION;
D O I：
10.1007/s11222-011-9286-4
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
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.
引用
收藏
页码：1125 / 1133
页数：9
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