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

Abstract

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.

12 pages

Cited by in corpus (2)

• Standard imsets for undirected and chain graphical models
• Recursive computation for evaluating the exact $p$-values of temporal and spatial scan statistics