Probability distributions fitting with Python

[Manuelito]

This post shows how to fit a probability distribution using the Scipy Library.

[imurray]

Summary: The fit method of the distributions in SciPy does maximum likelihood fitting of parameters to data. This blog post gives a couple of nice quick examples.

To see what else is available, one would go here: http://docs.scipy.org/doc/scipy/reference/stats.html

[sirclueless]

I just want to point out that "we can observe that they are really similar" isn't likely to impress a statistician. It's possible to make a ballpark estimate of how close they actually are because there is a graph with marked axes, but the real derived parameters would be nice to know, and maybe even do the legwork and calculate the expected error for your sample size.

[the_cat_kittles]

Thanks for the heads up on a "Rayleigh distribution" (from http://en.wikipedia.org/wiki/Rayleigh_distribution):

"One example where the Rayleigh distribution naturally arises is when wind speed is analyzed into its orthogonal 2-dimensional vector components. Assuming that the magnitude of each component is uncorrelated and normally distributed with equal variance, then the overall wind speed (vector magnitude) will be characterized by a Rayleigh distribution."

That is cool, I wonder if I will ever use it.

[probably]

This is regularly done in R using the MASS library.