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Measuring the Complexity of Continuous Distributions (1511.00529v1)
Published 2 Nov 2015 in nlin.AO, cond-mat.stat-mech, and cs.CC
Abstract: We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.