Measuring the Complexity of Continuous Distributions
arXiv:1511.00529 · doi:10.3390/e18030072
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.
21 pages, 5 Tables, 4 Figures
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