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A measure of statistical complexity based on predictive information

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arxiv 1012.1890 v1 pith:DZDKV46R submitted 2010-12-08 math.ST cs.ITmath.ITphysics.data-anstat.TH

Samer A. Abdallah , Mark D. Plumbley This is my paper
classification math.STcs.ITmath.ITphysics.data-anstat.TH
keywords informationbindingmeasurecomplexitymulti-informationpredictiveprocessesrandom
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We introduce an information theoretic measure of statistical structure, called 'binding information', for sets of random variables, and compare it with several previously proposed measures including excess entropy, Bialek et al.'s predictive information, and the multi-information. We derive some of the properties of the binding information, particularly in relation to the multi-information, and show that, for finite sets of binary random variables, the processes which maximises binding information are the 'parity' processes. Finally we discuss some of the implications this has for the use of the binding information as a measure of complexity.

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Cited by 4 Pith papers

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    Synergy-based measures of spacetime integration outperform current IIT practice when tested on simple deterministic networks.

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    Introduces four synergy-based measures of spacetime integration from partial information decomposition and finds them more suitable than current IIT practice for simple deterministic networks.

  4. Quantifying Spacetime Integration across a Partition with Synergy

    cs.IT 2026-04 unverdicted novelty 6.0 of 10

    Four synergy-based integration measures from partial information decomposition are shown to outperform current IIT integration measures on simple deterministic networks.

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