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 ...

Assume that we have some data D and a model M of the process that generated the data. The model has some parameters θ, the specific value of which we do not know but wish to estimate. If the model is properly constructed, we will be able to calculate the probability of it generating the observed data given a specific set of parameter values, P (D|θ,M). Often, the conditioning on the model is su...