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Maximizable Informational Entropy as Measure of Probabilistic Uncertainty by Ou, C. J.; Kaabouchi, A. El; Nivanen, L.; Tsobnang, F.; Méhauté, A. Le; Wang, Qiuping A. is a scholarly article available to read on EtoBox.

What is Maximizable Informational Entropy as Measure of Probabilistic Uncertainty about?

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.

Author

Ou, C. J.; Kaabouchi, A. El; Nivanen, L.; Tsobnang, F.; Méhauté, A. Le; Wang, Qiuping A.

Published

2008

Language

EN