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Entropy 2015, 17(7), 4644-4653; doi:10.3390/e17074644

Quantifying Redundant Information in Predicting a Target Random Variable

1
School of Computing, National University of Singapore, Singapore 119077, Singapore
2
Computer Science and Electrical Engineering, Caltech, Pasadena, CA 91125, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Rick Quax
Received: 18 March 2015 / Revised: 24 June 2015 / Accepted: 26 June 2015 / Published: 2 July 2015
(This article belongs to the Special Issue Information Processing in Complex Systems)
View Full-Text   |   Download PDF [3093 KB, uploaded 2 July 2015]   |  

Abstract

We consider the problem of defining a measure of redundant information that quantifies how much common information two or more random variables specify about a target random variable. We discussed desired properties of such a measure, and propose new measures with some desirable properties. View Full-Text
Keywords: synergy; information theory; complex systems; irreducibility; synergistic information; intersection-information synergy; information theory; complex systems; irreducibility; synergistic information; intersection-information
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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. (CC BY 4.0).

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Griffith, V.; Ho, T. Quantifying Redundant Information in Predicting a Target Random Variable. Entropy 2015, 17, 4644-4653.

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