  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.
