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

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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.

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Correspondence to Jianhua Guo.

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This research was supported by the National Natural Science Foundation of China (Grant Numbers 10871038, 10926186, 11025102 and 11101052), the National 973 Key Project of China (2007CB311002), the Jilin Project (20100401), the Research Grant Council of the Hong Kong Special Administrative Region (Project No. HKBU261508).

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Xu, PF., Guo, J. & Tang, ML. An improved Hara-Takamura procedure by sharing computations on junction tree in Gaussian graphical models. Stat Comput 22, 1125–1133 (2012). https://doi.org/10.1007/s11222-011-9286-4

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  • DOI: https://doi.org/10.1007/s11222-011-9286-4

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