Infinite mixtures of trees

Authors:
Sergey Kirshner;Padhraic Smyth
Affiliations:
University of Alberta, Edmonton, Canada;University of California, Irvine, CA
Venue:
Proceedings of the 24th international conference on Machine learning
Year:
2007

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Abstract

Finite mixtures of tree-structured distributions have been shown to be efficient and effective in modeling multivariate distributions. Using Dirichlet processes, we extend this approach to allow countably many tree-structured mixture components. The resulting Bayesian framework allows us to deal with the problem of selecting the number of mixture components by computing the posterior distribution over the number of components and integrating out the components by Bayesian model averaging. We apply the proposed framework to identify the number and the properties of predominant precipitation patterns in historical archives of climate data.