Estimation of Transfer Entropy between Discrete and Continuous Random Processes

Juliana Martins de Assis, Francisco Marcos de Assis


Transfer entropy is a measure of causality that has been widely applied and one of its identities is the sum of mutual information terms. In this article we evaluate two
existing methods of mutual information estimation in the specific application of detecting causality between a discrete random process and a continuous random process: binning method and nearest neighbours method. Simulated examples confirm, in the overall scenario, that the nearest neighbours method detects causality more reliably than the binning method.


Transfer entropy; causality; continuous process; discrete process; estimation; nearest neighbours; binning

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Copyright (c) 2018 Juliana Martins de Assis, Francisco Marcos de Assis

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