In this work we explore the Perturb and Combine idea celebrated in supervised learning in the context of probability density estimation in high-dimensional spaces. We propose a new family of unsupervised learning methods of mixtures of large ensembles of randomly generated poly-trees. The specific feature of these methods is their scalability to very large numbers of variables and training instances. We explore various variants of these methods empirically on a set of discrete test problems of growing complexity.