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Characterising high-order interdependence via entropic conjugation
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High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative information-theoretic quantities capturing high-order phenomena, but their interpretation and relationship with each other is not well understood. The lack of principles unifying these quantities obscures the choice of tools for enabling specific type of analyses. Here we show how an entropic conjugation provides a theoretically grounded principle to investigate the space of possible high-order quantities, clarifying the nature of the existent metrics while revealing gaps in the literature. This leads to identify novel notions of symmetry and skew-symmetry as key properties for guaranteeing a balanced account of high-order interdependencies and enabling broadly applicable analyses across physical systems.
Forward citations
Cited by 2 Pith papers
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Permutation-Free High-Order Interaction Tests
Kernel tests xdHSIC, xLI, xSI detect high-order joint independence and factorisation without permutations, with a claimed standard normal null.
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THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing
THOI is a PyTorch-based library that computes higher-order interaction metrics orders of magnitude faster than existing tools by batch-processing Gaussian-copula covariance matrices.
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