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

Ping Feng Xu, Jianhua Guo*, Man Lai TANG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1125-1133
Number of pages9
JournalStatistics and Computing
Volume22
Issue number5
DOIs
Publication statusPublished - Sep 2012

Scopus Subject Areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

User-Defined Keywords

  • Gaussian graphical model
  • HT procedure
  • Iterative proportional scaling
  • Junction tree
  • Sharing computations

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