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
收藏
页码：3461 / 3468
页数：8
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