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MAXENT3D_PID: An estimator for the maximum-entropy trivariate partial information decomposition

Makkeh, A., Chicharro, D. ORCID: 0000-0002-4038-258X, Theis, D. O. and Vicente, R. (2019). MAXENT3D_PID: An estimator for the maximum-entropy trivariate partial information decomposition. Entropy, 21(9), 862. doi: 10.3390/e21090862

Abstract

Partial information decomposition (PID) separates the contributions of sources about a target into unique, redundant, and synergistic components of information. In essence, PID answers the question of "who knows what" of a system of random variables and hence has applications to a wide spectrum of fields ranging from social to biological sciences. The paper presents MAXENT3D_PID, an algorithm that computes the PID of three sources, based on a recently-proposed maximum entropy measure, using convex optimization (cone programming). We describe the algorithm and its associated software utilization and report the results of various experiments assessing its accuracy. Moreover, the paper shows that a hierarchy of bivariate and trivariate PID allows obtaining the finer quantities of the trivariate partial information measure.

Publication Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: multivariate information theory; partial information decomposition; cone programming; synergy; redundancy; Python
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date available in CRO: 08 Dec 2021 10:09
Date deposited: 8 December 2021
Date of acceptance: 27 August 2019
Date of first online publication: 3 September 2019
URI: https://openaccess.city.ac.uk/id/eprint/27165
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