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Matrix normal distribution : ウィキペディア英語版
Matrix normal distribution

| cdf =
| mean =\mathbf
| median =
| mode =
| variance =\mathbf (among-row) and \mathbf (among-column)
| skewness =
| kurtosis =
| entropy =
| mgf =
| char =
}}
In statistics, the matrix normal distribution is a probability distribution that is a generalization of the multivariate normal distribution to matrix-valued random variables.
== Definition ==
The probability density function for the random matrix X (''n'' × ''p'') that follows the matrix normal distribution \mathcal_(\mathbf, \mathbf, \mathbf) has the form:
:
p(\mathbf\mid\mathbf, \mathbf, \mathbf) = \frac \, \mathrm\left(\mathbf^ (\mathbf - \mathbf)^ \mathbf^ (\mathbf - \mathbf) \right ) \right)}|^ |\mathbf|^}

where \mathrm denotes trace and M is ''n'' × ''p'', U is ''n'' × ''n'' and V is ''p'' × ''p''.
The matrix normal is related to the multivariate normal distribution in the following way:
:\mathbf \sim \mathcal_(\mathbf, \mathbf, \mathbf),
if and only if
:\mathrm(\mathbf) \sim \mathcal_(\mathrm(\mathbf), \mathbf \otimes \mathbf)
where \otimes denotes the Kronecker product and \mathrm(\mathbf) denotes the vectorization of \mathbf.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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