# scipy.stats.betabinom#

scipy.stats.betabinom = <scipy.stats._discrete_distns.betabinom_gen object>[source]#

A beta-binomial discrete random variable.

As an instance of the rv_discrete class, betabinom object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

Methods

 rvs(n, a, b, loc=0, size=1, random_state=None) Random variates. pmf(k, n, a, b, loc=0) Probability mass function. logpmf(k, n, a, b, loc=0) Log of the probability mass function. cdf(k, n, a, b, loc=0) Cumulative distribution function. logcdf(k, n, a, b, loc=0) Log of the cumulative distribution function. sf(k, n, a, b, loc=0) Survival function (also defined as 1 - cdf, but sf is sometimes more accurate). logsf(k, n, a, b, loc=0) Log of the survival function. ppf(q, n, a, b, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, n, a, b, loc=0) Inverse survival function (inverse of sf). stats(n, a, b, loc=0, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(n, a, b, loc=0) (Differential) entropy of the RV. expect(func, args=(n, a, b), loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(n, a, b, loc=0) Median of the distribution. mean(n, a, b, loc=0) Mean of the distribution. var(n, a, b, loc=0) Variance of the distribution. std(n, a, b, loc=0) Standard deviation of the distribution. interval(confidence, n, a, b, loc=0) Confidence interval with equal areas around the median.

Notes

The beta-binomial distribution is a binomial distribution with a probability of success p that follows a beta distribution.

The probability mass function for betabinom is:

$f(k) = \binom{n}{k} \frac{B(k + a, n - k + b)}{B(a, b)}$

for $$k \in \{0, 1, \dots, n\}$$, $$n \geq 0$$, $$a > 0$$, $$b > 0$$, where $$B(a, b)$$ is the beta function.

betabinom takes $$n$$, $$a$$, and $$b$$ as shape parameters.

References

The probability mass function above is defined in the “standardized” form. To shift distribution use the loc parameter. Specifically, betabinom.pmf(k, n, a, b, loc) is identically equivalent to betabinom.pmf(k - loc, n, a, b).

Examples

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


Calculate the first four moments:

>>> n, a, b = 5, 2.3, 0.63
>>> mean, var, skew, kurt = betabinom.stats(n, a, b, moments='mvsk')


Display the probability mass function (pmf):

>>> x = np.arange(betabinom.ppf(0.01, n, a, b),
...               betabinom.ppf(0.99, n, a, b))
>>> ax.plot(x, betabinom.pmf(x, n, a, b), 'bo', ms=8, label='betabinom pmf')
>>> ax.vlines(x, 0, betabinom.pmf(x, n, a, b), colors='b', lw=5, alpha=0.5)


Alternatively, the distribution object can be called (as a function) to fix the shape and location. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pmf:

>>> rv = betabinom(n, a, b)
>>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
...         label='frozen pmf')
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()


Check accuracy of cdf and ppf:

>>> prob = betabinom.cdf(x, n, a, b)
>>> np.allclose(x, betabinom.ppf(prob, n, a, b))
True


Generate random numbers:

>>> r = betabinom.rvs(n, a, b, size=1000)