`scipy.stats._continuous_distns:beta_gen`

source: /scipy/stats/_continuous_distns.py :708

Signature

   class     beta_gen (    momtype    =  1   ,    a    =  None   ,    b    =  None   ,    xtol    =  1e-14   ,    badvalue    =  None   ,    name    =  None   ,    longname    =  None   ,    shapes    =  None   ,    seed    =  None     )

Members

Summary

A beta continuous random variable.

Extended Summary

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Notes

The probability density function for beta is:

f (x, a, b) = \frac{Γ ( a + b ) x ^{a - 1} ( 1 - x ) ^{b - 1}}{Γ ( a ) Γ ( b )}

for $0 <= x <= 1$ , $a > 0$ , $b > 0$ , where $Γ$ is the gamma function (scipy.special.gamma).

beta takes $a$ and $b$ as shape parameters.

This distribution uses routines from the Boost Math C++ library for the computation of the pdf, cdf, ppf, sf and isf methods. ^[1]

Maximum likelihood estimates of parameters are only available when the location and scale are fixed. When either of these parameters is free, beta.fit resorts to numerical optimization, but this problem is unbounded: the location and scale may be chosen to make the minimum and maximum elements of the data coincide with the endpoints of the support, and the shape parameters may be chosen to make the PDF at these points infinite. For best results, pass floc and fscale keyword arguments to fix the location and scale, or use scipy.stats.fit with method='mse'.

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Aliases

scipy.stats._continuous_distns.beta_gen