Two-level Mixtures of Markov Trees

We study algorithms for learning Mixtures of Markov Trees for density estimation. There are two approaches to build such mixtures, which both exploit the interesting scaling properties of Markov Trees. We investigate whether the maximum likelihood and the variance reduction approaches can be combined together by building a two level Mixture of Markov Trees. Our experiments on synthetic data sets show that this two-level model outperforms the maximum likelihood one