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Affiliation of Author(s):数学与统计学院
Teaching and Research Group:统计教研室
Journal:Statistics and computing
Funded by:国家自然科学基金项目
Key Words:Gaussian graphical model · HT procedure · Iterative proportional scaling · Junction tree · Sharing computations
Abstract: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.
First Author:xupingfeng
Indexed by:Journal paper
Page Number:1
Translation or Not:no
Date of Publication:2012-09-01