  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.
