Characterising high-order interdependence via entropic conjugation

Autor:	Rosas, Fernando E., Gutknecht, Aaron, Mediano, Pedro A. M., Gastpar, Michael
Rok vydání:	2024
Předmět:	Computer Science - Information Theory Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.10485 Zobrazit plný text záznamu View this record from Arxiv