Constraint Based Learning of Mixtures of Trees
François Schnitzler, Louis A. Wehenkel · ORBi (University of Liège) · 2009
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.