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matrix normal distribution

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matrix normal distribution

The matrix normal distribution is a probability distribution that is a generalization of the normal distribution. The probability density function for the random matrix X (n × p) that follows the matrix normal distribution has the form

p(\mathbf{X}|\mathbf{M}, {\boldsymbol \Omega}, {\boldsymbol \Sigma}) =(2\pi)^{-np/2} |{\boldsymbol \Omega}|^{-p/2}  |{\boldsymbol  \Sigma}|^{-n/2} \exp\left(    -\frac{1}{2}    \mbox{tr}\left[      {\boldsymbol  \Omega}^{-1}      (\mathbf{X} - \mathbf{M})      {\boldsymbol  \Sigma}^{-1}      (\mathbf{X} - \mathbf{M})^{T}    \right]  \right).

See also

Image:Bvn-small.png Probability distributions []
Univariate Multivariate
Discrete: BenfordBernoullibinomialBoltzmanncategoricalcompound Poissondiscrete phase-typedegenerateGauss-Kuzmingeometrichypergeometriclogarithmicnegative binomialparabolic fractalPoissonRademacherSkellamuniformYule-SimonzetaZipfZipf-Mandelbrot Ewensmultinomialmultivariate Polya
Continuous: BetaBeta primeCauchychi-squareDirac delta functionCoxianErlangexponentialexponential powerFfadingFisher's zFisher-TippettGammageneralized extreme valuegeneralized hyperbolicgeneralized inverse GaussianHalf-LogisticHotelling's T-squarehyperbolic secanthyper-exponentialhypoexponentialinverse chi-square (scaled inverse chi-square)• inverse Gaussianinverse gamma (scaled inverse gamma) • KumaraswamyLandauLaplaceLévyLévy skew alpha-stablelogisticlog-normalMaxwell-BoltzmannMaxwell speednormal (Gaussian)normal-gammanormal inverse GaussianParetoPearsonphase-typepolarraised cosineRayleighrelativistic Breit-WignerRiceshifted GompertzStudent's ttriangulartype-1 Gumbeltype-2 GumbeluniformVariance-GammaVoigtvon MisesWeibullWigner semicircleWilks' lambda Dirichletinverse-WishartKentmatrix normalmultivariate normalmultivariate Studentvon Mises-FisherWigner quasiWishart
Miscellaneous: Cantorconditionalequilibriumexponential familyinfinitely divisiblelocation-scale familymarginalmaximum entropyposteriorpriorquasisamplingsingular

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