信息论的新领域

2014 RoEduNet Conference 13th Edition: Networking in Education and Research Joint Event RENAM 8th Conference Pub Date : 2014-11-13 DOI:10.1109/ROEDUNET-RENAM.2014.6955301

M. Popescu, E. Slusanschi, Voichita Iancu, Florin Pop

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引用次数: 1

摘要

离散值随机变量的香农熵在信息论中，特别是在信息的传输、处理和存储以及测量理论中占有重要地位，对数据集成和概率估计具有重要影响。本文的目的是通过对Jensen不等式的改进，给出香农熵的一个新的界。应用这种改进是为了找到一个新的和更准确的香农熵上界。在此基础上，提出了用连通图建模计算机网络结构复杂性分析的一个应用。

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A new bound in information theory

Shannon Entropy, for discrete-valued random variables, plays important roles in information theory [1], especially for the transmission, processing and storage of information and also in measure theory with major impact to data integration and probabilistic estimation. The purpose of this paper is to present a new bound for the Shannon Entropy, by first developing a refinement of Jensen's inequality. This refinement is applied in order to find a new and more accurate upper bound for Shannon Entropy. Based on this, the paper presents an application to structural complexity analysis of a computer network modeled by a connected graph.

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2014 RoEduNet Conference 13th Edition: Networking in Education and Research Joint Event RENAM 8th Conference

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