Constraint Based Learning of Mixtures of Trees

Mixtures of trees can be used to model any multivariate distributions. In this
work the possibility to learn these models from data by causal learning is explored. The
algorithm developed aims at approximating all first order relationships between pairs
of variables by a mixture of a given size. This approach is evaluated based on synthetic
data, and seems promising.