`scipy.stats._multivariate:matrix_t_gen.pdf`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_multivariate.py:1978

Signature

def pdf(self, X, mean = None, row_spread = 1, col_spread = 1, df = 1)

Summary

Matrix t probability density function.

Parameters

X : array_like: Quantiles, with the last two axes of X denoting the components.
mean : array_like, optional: Mean of the distribution (default: None)
row_spread : array_like, optional: Row-wise 2nd order raw central moment matrix (default: 1)
col_spread : array_like, optional: Column-wise 2nd order raw central moment matrix (default: 1)
df : scalar, optional: Degrees of freedom (default: 1)

Returns

pdf : ndarray: Probability density function evaluated at X

Notes

If mean is set to None then a matrix of zeros is used for the mean. The dimensions of this matrix are inferred from the shape of row_spread and col_spread, if these are provided, or set to 1 if ambiguous.

row_spread and col_spread can be two-dimensional array_likes specifying the spread matrices directly. Alternatively, a one-dimensional array will be be interpreted as the entries of a diagonal matrix, and a scalar or zero-dimensional array will be interpreted as this value times the identity matrix.

Examples

import numpy as np
from scipy.stats import matrix_t

✓

M = np.arange(6).reshape(3,2); M
Sigma = np.diag([1,2,3]); Sigma
Omega = 0.3*np.identity(2); Omega
X = M + 0.1; X

✗

array([[0.1, 1.1],
       [2.1, 3.1],
       [4.1, 5.1]])

df = 3; df

✓

matrix_t.logpdf(X, mean=M, row_spread=Sigma, col_spread=Omega, df=df)

✗

np.float64(-0.002715656044663173)

Aliases

scipy.stats._multivariate.matrix_t_gen.pdf