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A Probabilistic Upper Bound on Differential Entropy

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2 Author(s)
Learned-Miller, E. ; Dept. of Comput. Sci., Univ. of Massachusetts, Amherst, MA ; DeStefano, J.

A novel 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. 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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Information Theory, IEEE Transactions on  (Volume:54 ,  Issue: 11 )