PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Copula Bayesian Networks
Gal Elidan
Advances in Neural Information Processing Systems Volume 24, 2010.

Abstract

We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimat- ing univariate distributions. Using a novel copula-based reparameterization of a conditional density, joined with a graph that encodes independencies, our model offers great flexibility in modeling high-dimensional densities, while maintaining control over the form of the univariate marginals. We demonstrate the advantage of our framework for generalization over standard Bayesian networks as well as tree structured copula models for varied real-life domains that are of substantially higher dimension than those typically considered in the copula literature.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7058
Deposited By:Gal Elidan
Deposited On:25 February 2011