%0 Journal Article %J Entropy %D 2016 %T Measuring the Complexity of Continuous Distributions %A Santamaría-Bonfil, Guillermo %A Fernández, 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