Learning with Tree-Averaged Densities and Distributions

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Sergey Kirshner


We utilize the ensemble of trees framework, a tractable mixture over super- exponential number of tree-structured distributions [1], to develop a new model for multivariate density estimation. The model is based on a construction of tree- structured copulas – multivariate distributions with uniform on [0, 1] marginals. By averaging over all possible tree structures, the new model can approximate distributions with complex variable dependencies. We propose an EM algorithm to estimate the parameters for these tree-averaged models for both the real-valued and the categorical case. Based on the tree-averaged framework, we propose a new model for joint precipitation amounts data on networks of rain stations.