Title :
The relationship between transfer entropy and directed information
Author :
Liu, Ying ; Aviyente, Selin
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Abstract :
One challenging problem in the study of complex networks is the quantification of relationships between time series recorded across the network. Two information-theoretic measures, i.e., transfer entropy and directed information, have been extensively studied to capture the causality relationship between subsystems of a network. However, the relationship between these two measures have not been fully investigated to date. In this paper, we derive a formula to show the relationship between these two measures, in particular show that, transfer entropy is equal to the upper bound of directed information rate, and verify it through simulations.
Keywords :
entropy; time series; complex networks; directed information; information-theoretic measures; time series; transfer entropy; Entropy; Equations; Information rates; Markov processes; Mathematical model; Mutual information; Upper bound; Causality; Directed information; Transfer entropy;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location :
Ann Arbor, MI
Print_ISBN :
978-1-4673-0182-4
Electronic_ISBN :
pending
DOI :
10.1109/SSP.2012.6319809