Maximum Likelihood Estimation in Graphical Models with Missing Values

In this paper we discuss maximum likelihood estimation when some
observations are missing in mixed graphical interaction models
assuming a conditional Gaussian distribution as introduced by
Lauritzen&Wermuth (1989). For the saturated case ML estimation
with missing values via the EM algorithm has been proposed by
Little&Schluchter (1985). We expand their results to the
special restrictions in graphical models and indicate a more
efficient way to compute the E--step. The main purpose of the paper
is to show that for certain missing patterns the computational
effort can considerably be reduced.