statsmodels.distributions.mixture_rvs.mixture_rvs

statsmodels.distributions.mixture_rvs.mixture_rvs(prob, size, dist, kwargs=None, rng=None)

Sample from a mixture of distributions.

Parameters:

probarray_like

Probability of sampling from each distribution in dist

sizeint

The length of the returned sample.

distarray_like

An iterable of distributions objects from scipy.stats.

kwargstuple of dicts, optional

A tuple of dicts. Each dict in kwargs can have keys loc, scale, and args to be passed to the respective distribution in dist. If not provided, the distribution defaults are used.

rngint, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional

If rng is None, the legacy global (singleton) RandomState provided by numpy.random is used; this behavior is deprecated and will change to creating a new Generator using fresh entropy from the operating system in a future release. If rng is an int or array of ints, a new RandomState instance is created, seeded with rng. If rng is already a Generator or RandomState instance, that instance is used.

Returns:

ndarray

Sample from the mixture distribution, with length size.

Examples

Say we want 5000 random variables from mixture of normals with two distributions norm(-1,.5) and norm(1,.5) and we want to sample from the first with probability .75 and the second with probability .25.

>>> from scipy import stats
>>> prob = [.75,.25]
>>> Y = mixture_rvs(prob, 5000, dist=[stats.norm, stats.norm],
...                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))