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• W3100917360 startingPage "318" @default.
• W3100917360 abstract "The problem of how to properly quantify redundant information is an open question that has been the subject of much recent research. Redundant information refers to information about a target variable S that is common to two or more predictor variables X i . It can be thought of as quantifying overlapping information content or similarities in the representation of S between the X i . We present a new measure of redundancy which measures the common change in surprisal shared between variables at the local or pointwise level. We provide a game-theoretic operational definition of unique information, and use this to derive constraints which are used to obtain a maximum entropy distribution. Redundancy is then calculated from this maximum entropy distribution by counting only those local co-information terms which admit an unambiguous interpretation as redundant information. We show how this redundancy measure can be used within the framework of the Partial Information Decomposition (PID) to give an intuitive decomposition of the multivariate mutual information into redundant, unique and synergistic contributions. We compare our new measure to existing approaches over a range of example systems, including continuous Gaussian variables. Matlab code for the measure is provided, including all considered examples." @default.
• W3100917360 created "2020-11-23" @default.
• W3100917360 creator A5000252599 @default.
• W3100917360 date "2017-06-29" @default.
• W3100917360 modified "2023-10-06" @default.
• W3100917360 title "Measuring Multivariate Redundant Information with Pointwise Common Change in Surprisal" @default.
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• W3100917360 doi "https://doi.org/10.3390/e19070318" @default.
• W3100917360 hasPublicationYear "2017" @default.
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