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- Title
Entropy of Uncertain Random Variables wi h Application to Minimum Spanning Tree Problem.
- Authors
Sheng, Yuhong; Shi, Gang; Ralescu, Dan A.
- Abstract
Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicted. Based on the notion of chance measure, a concept of uncertain random entropy is introduced and used to provide a quantitative measurement of the uncertainty associated with uncertain random variables and its properties are studied in this paper. Relative entropy is a measure of the difference between two distribution functions. In order to deal with the divergence of uncertain random variables via chance distributions, this paper proposes also the relative entropy for uncertain random variables, as well as it investigates some mathematical properties of this concept. As an application, a model is presented to formulate a minimum spanning tree problem with uncertain random edge weights involving a relative entropy chance distribution. Finally, a numerical example of an uncertain random network is put forward to illustrate the effectiveness of the proposed model.
- Subjects
ENTROPY; RANDOM variables; SPANNING trees; PREDICTION models; DIVERGENCE theorem; MATHEMATICAL models
- Publication
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 2017, Vol 25, Issue 4, p497
- ISSN
0218-4885
- Publication type
Article
- DOI
10.1142/S0218488517500210