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Quantifying high-order interdependencies via multivariate extensions of the mutual information

Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar, and Henrik J. Jensen
Phys. Rev. E 100, 032305 – Published 13 September 2019

Abstract

This paper introduces a model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales. Our framework leverages various multivariate extensions of Shannon's mutual information, and introduces the O-information as a metric that is capable of characterizing synergy- and redundancy-dominated systems. The O-information is a symmetric quantity, and can assess intrinsic properties of a system without dividing its parts into “predictors” and “targets.” We develop key analytical properties of the O-information, and study how it relates to other metrics of high-order interactions from the statistical mechanics and neuroscience literature. Finally, as a proof of concept, we present an exploration on the relevance of statistical synergy in Baroque music scores.

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  • Received 31 December 2018

DOI:https://doi.org/10.1103/PhysRevE.100.032305

©2019 American Physical Society

Physics Subject Headings (PhySH)

General PhysicsStatistical Physics

Authors & Affiliations

Fernando E. Rosas1,2,*, Pedro A. M. Mediano3, Michael Gastpar4, and Henrik J. Jensen1,5

  • 1Centre of Complexity Science and Department of Mathematics, Imperial College London, London SW7 2AZ, England, United Kingdom
  • 2Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, England, United Kingdom
  • 3Department of Computing, Imperial College London, London SW7 2AZ, England, United Kingdom
  • 4School of Computer and Communication Sciences, École polytechnique fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
  • 5Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8502, Japan

  • *f.rosas@imperial.ac.uk

Article Text

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References

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Issue

Vol. 100, Iss. 3 — September 2019

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