Uncertainty in Artificial Intelligence
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Speedy Model Selection (SMS) for Copula Models
Yaniv Tenzer, Gal Elidan
Abstract:
We tackle the challenge of efficiently learning the structure of expressive multivariate real-valued densities of copula graphical models. We start by theoretically substantiating the conjecture that for many copula families the magnitude of Spearman's rank correlation coefficient is monotone in the expected contribution of an edge in network, namely the negative copula entropy. We then build on this theory and suggest a novel Bayesian approach that makes use of a prior over values of Spearman's rho for learning copula-based models that involve a mix of copula families. We demonstrate the generalization effectiveness of our highly efficient approach on sizable and varied real-life datasets.
Keywords:
Pages: 625-634
PS Link:
PDF Link: /papers/13/p625-tenzer.pdf
BibTex:
@INPROCEEDINGS{Tenzer13,
AUTHOR = "Yaniv Tenzer and Gal Elidan",
TITLE = "Speedy Model Selection (SMS) for Copula Models",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "625--634"
}


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