Nonparanormal Belief Propagation (NPNBP)
Gal Elidan, Cobi Cario · 2012
The empirical success of the belief propagation approximate inference algorithm has inspired numerous theoretical and algorithmic advances. Yet, for continuous non-Gaussian domains performing belief propagation remains a challenging task: recent innovations such as nonparametric or kernel belief propagation, while use-ful, come with a substantial computational cost and offer little theoretical guaran-tees, even for tree structured models. In this work we present Nonparanormal BP for performing efficient inference on distributions parameterized by a Gaussian copulas network and any univariate marginals. For tree structured networks, our approach is guaranteed to be exact for this powerful class of non-Gaussian mod-els. Importantly, the method is as efficient as standard Gaussian BP, and its con-vergence properties do not depend on the complexity of the univariate marginals, even when a nonparametric representation is used. 1