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Edgeworth Approximation of Multivariate Differential Entropy

marc{at}neuro.kuleuven.ac.be, K.U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, B-3000 Leuven, Belgium

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.

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