scipy.stats.gausshyper = <scipy.stats._continuous_distns.gausshyper_gen object at 0x2b45d2fe3450>[source]

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:


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.


The probability density function for gausshyper is:

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))


>>> from scipy.stats import gausshyper
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> a, b, c, z = 13.7637716041, 3.11896366487, 2.51459803502, 5.1811649904
>>> mean, var, skew, kurt = gausshyper.stats(a, b, c, z, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(gausshyper.ppf(0.01, a, b, c, z),
...               gausshyper.ppf(0.99, a, b, c, z), 100)
>>> ax.plot(x, gausshyper.pdf(x, a, b, c, z),
...          'r-', lw=5, alpha=0.6, label='gausshyper pdf')

Alternatively, freeze the distribution and display the frozen pdf:

>>> rv = gausshyper(a, b, c, z)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = gausshyper.ppf([0.001, 0.5, 0.999], a, b, c, z)
>>> np.allclose([0.001, 0.5, 0.999], gausshyper.cdf(vals, a, b, c, z))

Generate random numbers:

>>> r = gausshyper.rvs(a, b, c, z, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)

(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.
logpdf(x, a, b, c, z, loc=0, scale=1) Log of the probability density function.
cdf(x, a, b, c, z, loc=0, scale=1) Cumulative density function.
logcdf(x, a, b, c, z, loc=0, scale=1) Log of the cumulative density function.
sf(x, a, b, c, z, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate).
logsf(x, a, b, c, z, loc=0, scale=1) Log of the survival function.
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).
moment(n, a, b, c, z, loc=0, scale=1) Non-central moment of order n
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.
expect(func, a, b, c, z, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Expected value of a function (of one argument) with respect to the distribution.
median(a, b, c, z, loc=0, scale=1) Median of the distribution.
mean(a, b, c, z, loc=0, scale=1) Mean of the distribution.
var(a, b, c, z, loc=0, scale=1) Variance of the distribution.
std(a, b, c, z, loc=0, scale=1) Standard deviation of the distribution.
interval(alpha, a, b, c, z, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

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