"A new class of entropy estimators for multi-dimensional densities" by Erik G Learned-Miller

Selected Works of Erik G Learned-Miller

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A new class of entropy estimators for multi-dimensional densities

IEEE (2003)

Erik G Learned-Miller, University of Massachusetts - Amherst

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Abstract

We present a new class of estimators for approximating the entropy of multi-dimensional probability densities based on a sample of the density. These estimators extend the classic "m-spacing" estimators of Vasicek (1976) and others for estimating entropies of one-dimensional probability densities. Unlike plug-in estimators of entropy, which first estimate a probability density and then compute its entropy. our estimators avoid the difficult intermediate step of density estimation. For fixed dimension. the estimators an polynomial in the sample size. Similarities to consistent and asymptotically efficient one-dimensional estimators of entropy suggest that our estimators may sham these properties.

Keywords

entropy,
parameter estimation,
probability,
statistical analysis

Disciplines

Computer Sciences

Publication Date

April, 2003

Publisher Statement

doi: 10.1109/ICASSP.2003.1199463

Citation Information

Erik G Learned-Miller. "A new class of entropy estimators for multi-dimensional densities" IEEE (2003)
Available at: http://works.bepress.com/erik_learned_miller/14/