`scipy.stats._multivariate:matrix_t_gen.pdf`

source: /scipy/stats/_multivariate.py :1973

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

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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

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df = 3; df

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

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Aliases

scipy.stats._multivariate.matrix_t_gen.pdf