MAXIMIZABLE INFORMATIONAL ENTROPY AS A MEASURE OF PROBABILISTIC UNCERTAINTY

被引：7

作者：

Ou, Congjie ^{[1
,2
]}

El Kaabouchi, Aziz ^{[1
]}

Nivanen, Laurent ^{[1
]}

Chen, Jincan ^{[1
,3
,4
]}

Tsobnang, Franois ^{[1
]}

Le Mehaute, Alain ^{[1
]}

Wang, Qiuping A. ^{[1
]}

机构：

[1] Inst Super Mat & Mecan, F-72000 Le Mans, France

[2] Huaqiao Univ, Coll Informat & Engn, Quanzhou 362021, Peoples R China

[3] Xiamen Univ, Dept Phys, Xiamen 361005, Peoples R China

[4] Xiamen Univ, Inst Theoret Phys & Astrophys, Xiamen 361005, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODERN PHYSICS B | 2010年 / 24卷 / 17期

关键词：

Uncertainty; varentropy; virtual work principle; probability distribution; RELAXATION; GIBBS;

D O I：

10.1142/S0217979210054713

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

In this work, we consider a recently proposed entropy S defined by a variational relationship dI = d (x) over bar - (dx) over bar as a measure of uncertainty of random variable x. The entropy defined in this way underlies an extension of virtual work principle (dx) over bar = 0 leading to the maximum entropy d(I - (x) over bar) = 0. This paper presents an analytical investigation of this maximizable entropy for several distributions such as the stretched exponential distribution, kappa-exponential distribution, and Cauchy distribution.

引用

页码：3461 / 3468

页数：8

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