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

被引：0
|
作者：
Ping-Feng Xu
Jianhua Guo
Man-Lai Tang
机构：
[1] Northeast Normal University,School of Mathematics and Statistics
[2] Changchun University of Technology,School of Basic Science
[3] Northeast Normal University,Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics
[4] Hong Kong Baptist University,Department of Mathematics
来源：
Statistics and Computing | 2012年 / 22卷
关键词：
Gaussian graphical model; HT procedure; Iterative proportional scaling; Junction tree; Sharing computations;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：1125 / 1133
页数：8
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