01125nas a2200157 4500008004100000022001400041245005700055210005700112300000700169490000700176520066200183100003400845700002300879700002300902856004200925 2016 eng d a1099-430000aMeasuring the Complexity of Continuous Distributions0 aMeasuring the Complexity of Continuous Distributions a720 v183 aWe 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.1 aSantamarÃa-Bonfil, Guillermo1 aFernÃ¡ndez, Nelson1 aGershenson, Carlos uhttp://www.mdpi.com/1099-4300/18/3/72