On Thu, Aug 24, 2017 at 7:56 AM, Renato Fabbri <renato.fab...@gmail.com> wrote: > > On Thu, Aug 24, 2017 at 11:47 AM, Nathan Goldbaum <nathan12...@gmail.com> wrote: >> >> The latest version of numpy is 1.13. >> >> In this case, as described in the docs, a power function distribution is one with a probability desnity function of the form ax^(a-1) for x between 0 and 1. > > ok, let's try ourselves to relate the terms. > Would you agree that the "power function distribution" is a "power-law distribution" > in which the domain is restricted to be [0,1]?
I probably wouldn't. The coincidental similarity in functional form (domain and normalizing constants notwithstanding) obscures the very different mechanisms each represent. The ambiguous name of the method `power` instead of `power_function` is my fault. You have my apologies. -- Robert Kern
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