Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems
Quantitative Methods
2015-06-04 v2 Information Theory
math.IT
Data Analysis, Statistics and Probability
Abstract
We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character which can be associated to the sign of the contribution. For the sake of computational complexity, we adopt the assumption of Gaussianity and use the corresponding exact formula for the conditional mutual information. We report the application of the proposed methodology on two EEG data sets.
Keywords
Cite
@article{arxiv.1203.3037,
title = {Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems},
author = {S. Stramaglia and Guo-Rong Wu and M. Pellicoro and D. Marinazzo},
journal= {arXiv preprint arXiv:1203.3037},
year = {2015}
}