Estimation of Transfer Entropy between Discrete and Continuous Random Processes
Keywords:
Transfer entropy, causality, continuous process, discrete process, estimation, nearest neighbours, binning
Abstract
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.
Published
31-01-2018
How to Cite
de Assis, J., & de Assis, F. (2018). Estimation of Transfer Entropy between Discrete and Continuous Random Processes. Journal of Communication and Information Systems, 33(1). https://doi.org/10.14209/jcis.2018.1
Issue
Section
Regular Papers
Copyright (c) 2018 Juliana Martins de Assis, Francisco Marcos de Assis
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