title: Edgeworth approximation of multivariate differential entropy.
abstract: We develop the general, multivariate case of the Edgeworth approximation of differential entropy and show that it can be more accurate than the nearest-neighbor method in the multivariate case and that it scales better with sample size. Furthermore, we introduce mutual information estimation as an application.
authors.affiliation: K. U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, B-3000 Leuven, Belgium. marc@neuro.kuleuven.ac.be
authors.forename: Marc M
authors.initials: MM
authors.lastname: Van Hulle
mesh_terms: D000465:Algorithms; D019277:Entropy; D016571:Neural Networks, Computer