`scipy.stats._multivariate:multivariate_normal_frozen.init`

Signature

def __init__(self, mean = None, cov = 1, allow_singular = False, seed = None, maxpts = None, abseps = 1e-05, releps = 1e-05)

Create a frozen multivariate normal distribution.

mean : array_like, default: ``[0]``: Mean of the distribution.
cov : array_like, default: ``[1]``: Symmetric positive (semi)definite covariance matrix of the distribution.
allow_singular : bool, default: ``False``: Whether to allow a singular covariance matrix.
seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional: If seed is None (or np.random), the numpy.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a Generator or RandomState instance then that instance is used.
maxpts : int, optional: The maximum number of points to use for integration of the cumulative distribution function (default 1000000*dim)
abseps : float, optional: Absolute error tolerance for the cumulative distribution function (default 1e-5)
releps : float, optional: Relative error tolerance for the cumulative distribution function (default 1e-5)

When called with the default parameters, this will create a 1D random variable with mean 0 and covariance 1:

from scipy.stats import multivariate_normal
r = multivariate_normal()

✓

r.mean

✗

array([0.])

r.cov

✓

array([[1.]])