Quasi-continuous maximum entropy distribution approximation with kernel density
by Thomas Mazzoni, Elmar Reucher
International Journal of Information and Decision Sciences (IJIDS), Vol. 3, No. 4, 2011

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.

Online publication date: Thu, 30-Oct-2014

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