A Gauss hypergeometric continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters :

x : array-like


q : array-like

lower or upper tail probability

a, b, c, z : array-like

shape parameters

loc : array-like, optional

location parameter (default=0)

scale : array-like, optional

scale parameter (default=1)

size : int or tuple of ints, optional

shape of random variates (default computed from input arguments )

moments : str, optional

composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’)

Alternatively, the object may be called (as a function) to fix the shape, :

location, and scale parameters returning a “frozen” continuous RV object: :

rv = gausshyper(a, b, c, z, loc=0, scale=1) :

  • Frozen RV object with the same methods but holding the given shape, location, and scale fixed.


Gauss hypergeometric distribution

gausshyper.pdf(x,a,b,c,z) = C * x**(a-1) * (1-x)**(b-1) * (1+z*x)**(-c) for 0 <= x <= 1, a > 0, b > 0, and C = 1/(B(a,b)F[2,1](c,a;a+b;-z))


>>> import matplotlib.pyplot as plt
>>> numargs = gausshyper.numargs
>>> [ a, b, c, z ] = [0.9,] * numargs
>>> rv = gausshyper(a, b, c, z)

Display frozen pdf

>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = gausshyper.cdf(x, a, b, c, z)
>>> h = plt.semilogy(np.abs(x - gausshyper.ppf(prb, a, b, c, z)) + 1e-20)

Random number generation

>>> R = gausshyper.rvs(a, b, c, z, size=100)

(Source code)


rvs(a, b, c, z, loc=0, scale=1, size=1) Random variates.
pdf(x, a, b, c, z, loc=0, scale=1) Probability density function.
cdf(x, a, b, c, z, loc=0, scale=1) Cumulative density function.
sf(x, a, b, c, z, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate).
ppf(q, a, b, c, z, loc=0, scale=1) Percent point function (inverse of cdf — percentiles).
isf(q, a, b, c, z, loc=0, scale=1) Inverse survival function (inverse of sf).
stats(a, b, c, z, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(a, b, c, z, loc=0, scale=1) (Differential) entropy of the RV.
fit(data, a, b, c, z, loc=0, scale=1) Parameter estimates for generic data.

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