`scipy.stats._multivariate:multivariate_normal_gen.logcdf`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_multivariate.py:708

Signature

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

Summary

Log of the 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 : int, 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: Log of the 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.logcdf