Technion, IEM faculty - Statistics Seminar
Speaker: Gal Elidan
Title: Copula Networks
Date: 03/04/2011
Time: 14:30
Place: Bloomfield-527
Abstract:  <>
Or here it is, more or less:
Gal Elidan
Multivariate continuous densities are of paramount important in
numerous fields ranging from computational biology to geology.
Bayesian networks offer a general framework geared toward estimation
of such densities in high-dimension by relying on a graph structure
that encodes independencies, facilitating a decomposition of the
likelihood and relatively efficient inference. However, practical
considerations almost always lead to a rather simple parametric form,
thereby limiting our ability to capture complex dependence
structures. In contrast, copulas offer great flexibility by providing
a generic representation of multivariate distributions that separates
the choice the marginal densities and that of the dependency
structure. Yet, despite a dramatic growth in academic and practical
interest, copulas are for the most part practical only for relatively
small (<10) dimensions.
I will present the Copula Network model, an elegant marriage between these
two frameworks. Our approach builds on a novel copula-based
re-parameterization of a conditional density that, joined with a graph
that encodes independencies, offers great flexibility in modeling and
estimation of high-dimensional domains, while maintaining control over
the form of the univariate marginals. I will 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.
Technion Math. Net (TECHMATH)
Editor: Michael Cwikel   <> 
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