statsmodels.distributions.mixture_rvs.mixture_rvs#

statsmodels.distributions.mixture_rvs.mixture_rvs(prob, size, dist, kwargs=None, rng=None)[source]#

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.
rng{None, numpy.random.Generator, numpy.random.RandomState}, optional: If rng is None, the global (legacy) NumPy random state is used. If rng is already a Generator or RandomState instance, that instance is used.

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)))