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 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.
|Original language||American English|
|Number of pages||10|
|State||Published - 2013|
|Event||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States|
Duration: 11 Jul 2013 → 15 Jul 2013
|Conference||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013|
|Period||11/07/13 → 15/07/13|