power laws
DESCRIPTION
Power Laws. Otherwise known as any semi-straight line on a log-log plot. Self Similar. The distribution maintains its shape This is the only distribution with this property. Fitting a line. Assumptions of linear Regression do not hold: noise is not Gaussian - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/1.jpg)
Power Laws
Otherwise known as any semi-straight line on a log-log plot
![Page 2: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/2.jpg)
![Page 3: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/3.jpg)
Self Similar
• The distribution maintains its shape
• This is the only distribution with this property
![Page 4: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/4.jpg)
Fitting a line
• Assumptions of linear Regression do not hold: noise is not Gaussian
• Many distributions approximate power laws, leading to high R2 indepent of the quality of the fit
• Regressions will not be properly normalized
![Page 5: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/5.jpg)
Maximum Likelihood Estimator for the continuous case
• α is greater than 1 – necessary for convergence• There is some xmin below which power law
behavior does not occur – necessary for convergence
• Converges as n→∞• This will give the best power law, but does not
test if a power law is a good distribution!!!
![Page 6: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/6.jpg)
How Does it do?
Actual Value: 2.5
Continuous
Discreet
![Page 7: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/7.jpg)
Error as a function of Xmin and n
For Discreet Data For Continous Data
![Page 8: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/8.jpg)
Setting Xmin
• Too low: we include non power-law data• Too high: we lose a lot of data• Clauset suggests “the value xmin that
makes the probability distributions between the measured data and the best-fit power-law model as similar as possible above xmin”
• Use KS statistic
![Page 9: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/9.jpg)
How does it perform?
![Page 10: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/10.jpg)
But How Do We Know it’s a Power Law?
• Calculate KS Statistic between data and best fitting power law
• Find p-value – theoretically, there exists a function p=f(KS value)
• But, the best fit distribution is not the “true” distribution due to statistical fluctuations
• Do a numerical approach: create distributions and find their KS value
• Compare D value to best fit value for each data set• We can now rule out a power law, but can we conclude
that it is a power law?
![Page 11: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/11.jpg)
Comparison of Models
• Which of two fits is least bad• Compute likelihood (R) of two distributions,
higher likelihood = better fit• But, we need to know how large statistical
fluctuations will be• Using central limit theroem, R will be normally
distributed – we can calculate p values from the standard deviation
![Page 12: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/12.jpg)
How does real world data stack up?
![Page 13: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/13.jpg)
Mechanisms
• Summation of exponentials
• Random walk – often first return
• The Yule process, whereby probabilities are related to the number that are already present
• Self-organized criticality – the burning forest
![Page 14: Power Laws](https://reader035.vdocuments.us/reader035/viewer/2022081603/56813651550346895d9dd5dd/html5/thumbnails/14.jpg)
Conclusions
• It’s really hard to show something is a power law
• With high noise or few points, it’s hard to show something isn’t a power law