scipy.stats.nbinom

scipy.stats.nbinom = <scipy.stats.distributions.nbinom_gen object at 0x4dd2dd0>

A negative binomial discrete random variable.

Discrete 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

quantiles

q : array_like

lower or upper tail probability

n, p : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

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 and

location parameters returning a “frozen” discrete RV object:

rv = nbinom(n, p, loc=0)

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

Notes

The probability mass function for nbinom is:

nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k

for k >= 0.

nbinom takes n and p as shape parameters.

Examples

>>> from scipy.stats import nbinom
>>> [ n, p ] = [<Replace with reasonable values>]
>>> rv = nbinom(n, p)

Display frozen pmf

>>> x = np.arange(0, np.minimum(rv.dist.b, 3))
>>> h = plt.vlines(x, 0, rv.pmf(x), lw=2)

Here, rv.dist.b is the right endpoint of the support of rv.dist.

Check accuracy of cdf and ppf

>>> prb = nbinom.cdf(x, n, p)
>>> h = plt.semilogy(np.abs(x - nbinom.ppf(prb, n, p)) + 1e-20)

Random number generation

>>> R = nbinom.rvs(n, p, size=100)

Methods

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