Measuring Multivariate Redundant Information with Pointwise Common Change in Surprisal

点式的 多元统计 计算机科学 数学 数据挖掘 人工智能 计量经济学 统计 数学分析
作者
Robin A. A. Ince
出处
期刊:Entropy [MDPI AG]
卷期号:19 (7): 318-318 被引量:157
标识
DOI:10.3390/e19070318
摘要

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.

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