TY - JOUR
T1 - Measuring the Complexity of Continuous Distributions
JF - Entropy
Y1 - 2016
A1 - SantamarÃa-Bonfil, Guillermo
A1 - FernÃ¡ndez, Nelson
A1 - Gershenson, Carlos
AB - 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.
VL - 18
UR - http://www.mdpi.com/1099-4300/18/3/72
ER -