Title: Quasi-continuous maximum entropy distribution approximation with kernel density
Authors: Thomas Mazzoni, Elmar Reucher
Addresses: Department of Statistics, University of Hagen Profilstr. 8, Hagen, 58084, Germany. ' Department of Operations Research, University of Hagen, Profilstr. 8, Hagen, 58084, Germany
Abstract: This paper extends maximum entropy estimation of discrete probability distributions to the continuous case. This transition leads to a non-parametric estimation of a probability density function, preserving the maximum entropy principle. Furthermore, the derived density estimate provides a minimum mean integrated square error. In the second step, it is shown how boundary conditions can be included, resulting in a probability density function obeying maximum entropy. The criterion for deviation from a reference distribution is the Kullback-Leibler entropy. It is further shown, how the characteristics of a particular distribution can be preserved by using integration kernels with mimetic properties.
Keywords: maximum entropy; MaxEnt; Kullback-Leibler entropy; kernel density estimation; mean integrated spare error; mimetic properties.
DOI: 10.1504/IJIDS.2011.043026
International Journal of Information and Decision Sciences, 2011 Vol.3 No.4, pp.335 - 350
Published online: 30 Oct 2014 *
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