A Probabilistic Upper Bound on Differential Entropy

Joseph DeStefano; Erik Learned-Miller

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A Probabilistic Upper Bound on Differential Entropy

Joseph DeStefano·Erik Learned-Miller

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Abstract

A novel, non-trivial, probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required, nor is the distribution required to have a density. Previous distribution-free bounds on the cumulative distribution function of a random variable given a sample of that variable are used to construct the bound. A simple, fast, and intuitive algorithm for computing the entropy bound from a sample is provided.

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Joseph DeStefano, Erik Learned-Miller. 2005-04-21. A Probabilistic Upper Bound on Differential Entropy. https://arxiv.org/abs/cs/0504091

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