Learning Mixtures of Tree Graphical Models

Abstract

We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables. We propose a novel method for estimating the mixture components with provable guarantees. Our output is a tree-mixture model which serves as a good approximation to the underlying graphical model mixture. The sample and computational requirements for our method scale as poly(p,r), for an r-component mixture of p-variate graphical models, for a wide class of models which includes tree mixtures and mixtures over bounded degree graphs.

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