`scipy.stats._multivariate:multivariate_normal_gen.cdf`

source: /scipy/stats/_multivariate.py :759

Signature

   def     cdf (    self    ,    x    ,    mean    =  None   ,    cov    =  1   ,    allow_singular    =  False   ,    maxpts    =  None   ,    abseps    =  1e-05   ,    releps    =  1e-05   ,  * ,     lower_limit    =  None   ,    rng    =  None     )

Summary

Multivariate normal cumulative distribution function.

Parameters

x : array_like: Quantiles, with the last axis of x denoting the components.
mean : array_like, default: ``[0]``: Mean of the distribution.
cov : array_like or `Covariance`, default: ``[1]``: Symmetric positive (semi)definite covariance matrix of the distribution.
allow_singular : bool, default: ``False``: Whether to allow a singular covariance matrix. This is ignored if cov is a Covariance object.
maxpts : integer, optional: The maximum number of points to use for integration (default 1000000*dim)
abseps : float, optional: Absolute error tolerance (default 1e-5)
releps : float, optional: Relative error tolerance (default 1e-5)
lower_limit : array_like, optional: Lower limit of integration of the cumulative distribution function. Default is negative infinity. Must be broadcastable with x.
rng : Generator, optional: an instance of np.random.Generator, which is used internally for QMC integration.

Returns

cdf : ndarray or scalar: Cumulative distribution function evaluated at x

Notes

Setting the parameter mean to None is equivalent to having mean be the zero-vector. The parameter cov can be a scalar, in which case the covariance matrix is the identity times that value, a vector of diagonal entries for the covariance matrix, a two-dimensional array_like, or a Covariance object.

Aliases

scipy.stats._multivariate.multivariate_normal_gen.cdf