scipy.stats.bernoulli

scipy.stats.bernoulli = <scipy.stats._discrete_distns.bernoulli_gen object>

A Bernoulli discrete random variable.

As an instance of the rv_discrete class, bernoulli 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.

Notes

The probability mass function for bernoulli is:

\[\begin{split}f(k) = \begin{cases}1-p &\text{if } k = 0\\ p &\text{if } k = 1\end{cases}\end{split}\]

for \(k\) in \(\{0, 1\}\), \(0 \leq p \leq 1\)

bernoulli takes \(p\) as shape parameter, where \(p\) is the probability of a single success and \(1-p\) is the probability of a single failure.

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

Examples

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

Calculate the first four moments:

>>> p = 0.3
>>> mean, var, skew, kurt = bernoulli.stats(p, moments='mvsk')

Display the probability mass function (pmf):

>>> x = np.arange(bernoulli.ppf(0.01, p),
...               bernoulli.ppf(0.99, p))
>>> ax.plot(x, bernoulli.pmf(x, p), 'bo', ms=8, label='bernoulli pmf')
>>> ax.vlines(x, 0, bernoulli.pmf(x, p), 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 = bernoulli(p)
>>> 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 = bernoulli.cdf(x, p)
>>> np.allclose(x, bernoulli.ppf(prob, p))
True

Generate random numbers:

>>> r = bernoulli.rvs(p, size=1000)

Methods

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

scipy.stats.multivariate_hypergeom scipy.stats.betabinom