Speedy Model Selection (SMS) for Copula Models

نویسندگان

  • Yaniv Tenzer
  • Gal Elidan
چکیده

We tackle the challenge of efficiently learning the structure of expressive multivariate realvalued 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 monotonic 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.

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عنوان ژورنال:
  • CoRR

دوره abs/1309.6867  شماره 

صفحات  -

تاریخ انتشار 2013