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