Quasi-continuous maximum entropy distribution approximation with kernel density

Thomas Mazzoni and Elmar Reucher

International Journal of Information and Decision Sciences, 2011, vol. 3, issue 4, 335-350

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. (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:3:y:2011:i:4:p:335-350

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