Parameter learning in MTE networks using incomplete data

Bayesian networks with mixtures of truncated exponentials (MTEs) are gaining popularity as a flexible modelling framework for hybrid domains. MTEs support efficient and exact inference algorithms, but estimating an MTE from data has turned out to be a difficult task. Current methods suffer from a considerable computational burden as well as the inability to handle missing values in the training data. In this paper we describe an EM-based algorithm for learning the maximum likelihood parameters of an MTE network when confronted with incomplete data. In order to overcome the computational difficulties we make certain distributional assumptions about the domain being modeled, thus focusing on a subclass of the general class of MTE networks. Preliminary empirical results indicate that the proposed method offers results that are inline with intuition.