%0 Journal Article
%J Entropy
%D 2016
%T Measuring the Complexity of Continuous Distributions
%A SantamarÃa-Bonfil, Guillermo
%A FernÃ¡ndez, Nelson
%A Gershenson, Carlos
%X We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon's information, the novel continuous complexity measures describe how a system's predictability changes in terms of the probability distribution parameters. 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.
%B Entropy
%V 18
%P 72
%G eng
%U http://www.mdpi.com/1099-4300/18/3/72
%R 10.3390/e18030072