`scipy.stats._multivariate:multivariate_t_gen.cdf`

source: /scipy/stats/_multivariate.py :5296

Signature

   def     cdf (    self    ,    x    ,    loc    =  None   ,    shape    =  1   ,    df    =  1   ,    allow_singular    =  False   ,  * ,     maxpts    =  None   ,    lower_limit    =  None   ,    random_state    =  None     )

Summary

Multivariate t-distribution cumulative distribution function.

Parameters

x : array_like: Points at which to evaluate the cumulative distribution function.
loc : array_like, optional: Location of the distribution. (default 0)
shape : array_like, optional: Positive semidefinite matrix of the distribution. (default 1)
df : float, optional: Degrees of freedom of the distribution; must be greater than zero. If np.inf then results are multivariate normal. The default is 1.
allow_singular : bool, optional: Whether to allow a singular matrix. (default False)
maxpts : int, optional: Maximum number of points to use for integration. The default is 1000 times the number of dimensions.
lower_limit : array_like, optional: Lower limit of integration of the cumulative distribution function. Default is negative infinity. Must be broadcastable with x.
seed : {None, int, np.random.RandomState, np.random.Generator}, optional: Used for drawing random variates. If seed is None, the ~np.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a RandomState or Generator instance, then that object is used. Default is None.

Returns

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

Examples

from scipy.stats import multivariate_t
x = [0.4, 5]
loc = [0, 1]
shape = [[1, 0.1], [0.1, 1]]
df = 7

✓

multivariate_t.cdf(x, loc, shape, df)

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Aliases

scipy.stats._multivariate.multivariate_t_gen.cdf