Parameter estimation of dependence tree models using the EM algorithm

A dependence tree is a model for the joint probability distribution of an n-dimensional random vector, which requires a relatively small number of free parameters by making Markov-like assumptions on the tree. The authors address the problem of maximum likelihood estimation of dependence tree models with missing observations, using the expectation-maximization algorithm. The solution involves computing observation probabilities with an iterative "upward-downward" algorithm, which is similar to an algorithm proposed for belief propagation in causal trees, a special case of Bayesian networks.<>