Fast iterative proportional scaling for Gaussian graphical models

In Gaussian graphical models, the likelihood equations must typically be solved iteratively, for example by iterative proportional scaling. However, this method may not scale well to models with many variables because it involves repeated inversion of large matrices. We present a version of the algorithm which avoids these inversions, resulting in increased speed, in particular when graphs are sparse.