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Information concentration for convex measures

Jiange Li 1 Matthieu Fradelizi 2 Mokshay Madiman 1
1 University of Delaware - Department of Mathematical Sciences
2 LAMA - Laboratoire d'Analyse et de Mathématiques Appliquées
Abstract : Sharp exponential deviation estimates of the information content as well as a sharp bound on the varentropy are obtained for convex probability measures on Euclidean spaces. These provide, in a sense, a nonasymptotic equipartition property for convex measures even in the absence of stationarity-type assumptions.
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Jiange Li, Matthieu Fradelizi, Mokshay Madiman. Information concentration for convex measures. 2016 IEEE International Symposium on Information Theory (ISIT 2016), Jul 2016, Barcelone, Spain. pp.1128-1132, ⟨10.1109/ISIT.2016.7541475⟩. ⟨hal-01590229⟩

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