Maximizable informational entropy as measure of probabilistic uncertainty
Statistical Mechanics
2020-10-28 v1
Abstract
In this work, we consider a recently proposed entropy S (called varentropy) defined by a variational relationship dI=beta*(d<x>-<dx>) as a measure of uncertainty of random variable x. By definition, varentropy underlies a generalized virtual work principle <dx>=0 leading to maximum entropy d(I-beta*<x>)=0. This paper presents an analytical investigation of this maximizable entropy for several distributions such as stretched exponential distribution, kappa-exponential distribution and Cauchy distribution.
Keywords
Cite
@article{arxiv.0803.3110,
title = {Maximizable informational entropy as measure of probabilistic uncertainty},
author = {C. J. Ou and A. El Kaabouchi and L. Nivanen and F. Tsobnang and A. Le Méhauté and Qiuping A. Wang},
journal= {arXiv preprint arXiv:0803.3110},
year = {2020}
}