Measuring the Complexity of Continuous Distributions
AbstractWe 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Santamaría-Bonfil, G.; Fernández, N.; Gershenson, C. Measuring the Complexity of Continuous Distributions. Entropy 2016, 18, 72.
Santamaría-Bonfil G, Fernández N, Gershenson C. Measuring the Complexity of Continuous Distributions. Entropy. 2016; 18(3):72.Chicago/Turabian Style
Santamaría-Bonfil, Guillermo; Fernández, Nelson; Gershenson, Carlos. 2016. "Measuring the Complexity of Continuous Distributions." Entropy 18, no. 3: 72.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.