Non–Parametric Estimation of Mutual Information through the Entropy of the Linkage
作者: Maria Teresa GiraudoLaura SacerdoteRoberta Sirovich
作者单位: 1Department of Mathematics, University of Torino, Via Carlo Alberto 10, Torino 10123, Italy
刊名: Entropy, 2013, Vol.15 (12), pp.5154-5177
来源数据库: Directory of Open Access Journals
DOI: 10.3390/e15125154
关键词: Information measuresMutual informationEntropyCopula functionLinkage functionKernel methodBinless estimator
原始语种摘要: A new, non–parametric and binless estimator for the mutual information of a d–dimensional random vector is proposed. First of all, an equation that links the mutual information to the entropy of a suitable random vector with uniformly distributed components is deduced. When d = 2 this equation reduces to the well known connection between mutual information and entropy of the copula function associated to the original random variables. Hence, the problem of estimating the mutual information of the original random vector is reduced to the estimation of the entropy of a random vector obtained through a multidimensional transformation. The estimator we propose is a two–step method: first estimate the transformation and obtain the transformed sample, then estimate its entropy. The properties...
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  • estimator 估计量
  • through 经过
  • information 报告
  • entropy 平均信息量
  • Non discrimination Taxation税收无差别待遇
  • estimate 估计
  • mutual 互相
  • unbiased 不偏性
  • estimating 价值估计
  • copula 基鳃骨