Original scientific paper
Un upper bound for Kullback-Leibler divergence with a small number of outliers
Alexander Gofman
; Faculty of Economics, Moscow Economics National Research University, Moscow
Mark Kelbert
; Department of Mathematics, Swansea University, Singleton Park, Swansea, UK
Abstract
We establish a new upper bound for the Kullback-Leibler divergence of two discrete probability distributions which are close in a sense that typically the ratio of probabilities is nearly one and the number of outliers is small.
Keywords
Kullback-Leibler divergence; relative entropy; mutual information; information inequality
Hrčak ID:
101400
URI
Publication date:
10.5.2013.
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