'Create a frozen multivariate normal distribution.
Parameters
mean : array_like, optional
Mean of the distribution (default zero)
cov : array_like, optional
Covariance matrix of the distribution (default one)
allow_singular : bool, optional
If this flag is True then tolerate a singular
covariance matrix (default False).
seed : None or int or np.random.RandomState instance, optional
This parameter defines the RandomState object to use for drawing
random variates.
If None (or np.random), the global np.random state is used.
If integer, it is used to seed the local RandomState instance
Default is None.
maxpts: integer, 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)
Examples
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.]])'

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

    self._dist = multivariate_normal_gen(seed)
    (self.dim, self.mean, self.cov) = self._dist._process_parameters(None, mean, cov)
    self.cov_info = _PSD(self.cov, allow_singular=allow_singular)
    if (not maxpts):
        maxpts = (1000000 * self.dim)
    self.maxpts = maxpts
    self.abseps = abseps
    self.releps = releps
