Quantifying unique information

By Nils Bertschinger, J. Rauh, E. Olbrich, Jürgen Jost, Nihat Ay. Entropy, 2013

342 citations recorded by the source corpus.

We propose new measures of shared information, unique information and synergistic information that can be used to decompose the mutual information of a pair of random variables (Y, Z) with a third random variable X. Our measures are motivated by an operational idea of unique information, which suggests that shared information and unique information should depend only on the marginal distributions of the pairs (X, Y) and (X,Z). Although this invariance property has not been studied before, it is satisfied by other proposed measures of shared information. The invariance property does not uniquely determine our new measures, but it implies that the functions that we define are bounds to any other measures satisfying the same invariance property. We study properties of our measures and compare them to other candidate measures.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8e53e6db19238f223bb1d42a616ee7b3324aa6ad

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In Computer Networks and Communications

In Statistics and Probability

Citations in the corpus

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, background): “While it is not clear to what degree such cancellation might occur in neuroimaging recordings this is a question that has recently received much interest [Bertschinger et al., 2014; Griffith and Koch, 2012; Harder et al., 2012; Olbrich et al., 2015; Timme et al., 2013; Williams and Beer, 2010].”