Estimation of Transfer Entropy between Discrete and Continuous Random Processes

Authors

  • Juliana Martins de Assis Universidade Federal de Campina Grande
  • Francisco Marcos de Assis Universidade Federal de Campina Grande

DOI:

https://doi.org/10.14209/jcis.2018.1

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.

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Author Biographies

Juliana Martins de Assis, Universidade Federal de Campina Grande

Departamento de Engenharia Elétrica

Francisco Marcos de Assis, Universidade Federal de Campina Grande

Departamento de Engenharia Elétrica

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Published

2018-01-31

How to Cite

de Assis, J. M., & de Assis, F. M. (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

Vol. 33 No. 1 (2018)

Section

Regular Papers

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Received 2017-07-20
Accepted 2018-01-08
Published 2018-01-31