  Graphical models with bi-directed edges (<->) represent marginal
independence: the absence of an edge between two vertices indicates that the
corresponding variables are marginally independent. In this paper, we consider
maximum likelihood estimation in the case of continuous variables with a
Gaussian joint distribution, sometimes termed a covariance graph model. We
present a new fitting algorithm which exploits standard regression techniques
and establish its convergence properties. Moreover, we contrast our procedure
to existing estimation methods.
