A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models

Details A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models

2008-02-19

arxiv.org/abs/0802.2581v2

Communications in Statistics Theory and Methods, 39, No.8, 1643-1654, 2010

Statistics

Computation

12 pages

Scientific paper

10.1080/03610920802238662

We discuss an efficient implementation of the iterative proportional scaling procedure in the multivariate Gaussian graphical models. We show that the computational cost can be reduced by localization of the update procedure in each iterative step by using the structure of a decomposable model obtained by triangulation of the graph associated with the model. Some numerical experiments demonstrate the competitive performance of the proposed algorithm.

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