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 language | English |
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Pages (from-to) | 1125-1133 |
Number of pages | 9 |
Journal | Statistics and Computing |
Volume | 22 |
Issue number | 5 |
DOIs | |
Publication status | Published - Sept 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