The Wishart distribution on a random positive-definite matrix {\boldsymbol{X}}_{q\times q} is is denoted {\boldsymbol{X}}\sim \operatorname{Wish}({\boldsymbol{\Psi}}, \nu), and defined as {\boldsymbol{X}}= ({\boldsymbol{L}}{\boldsymbol{Z}})({\boldsymbol{L}}{\boldsymbol{Z}})', where:
{\boldsymbol{\Psi}}_{q\times q} = {\boldsymbol{L}}{\boldsymbol{L}}' is the positive-definite matrix scale parameter,
\nu > q is the shape parameter,
{\boldsymbol{Z}}_{q\times q} is a random lower-triangular matrix with elements
Z_{ij} \begin{cases} \overset{\;\textrm{iid}\;}{\sim}\operatorname{Normal}(0,1) & i < j \\ \overset{\:\textrm{ind}\:}{\sim}\chi^2_{(\nu-i+1)} & i = j \\ = 0 & i > j. \end{cases}
The log-density of the Wishart distribution is
\log p({\boldsymbol{X}}\mid {\boldsymbol{\Psi}}, \nu) = -\textstyle{\frac{1}{2}} \left[\mathrm{tr}({\boldsymbol{\Psi}}^{-1} {\boldsymbol{X}}) + (q+1-\nu)\log |{\boldsymbol{X}}| + \nu \log |{\boldsymbol{\Psi}}| + \nu q \log(2) + 2 \log \Gamma_q(\textstyle{\frac{\nu }{2}})\right],
where \Gamma_n(x) is the multivariate Gamma function defined as
\Gamma_n(x) = \pi^{n(n-1)/4} \prod_{j=1}^n \Gamma\big(x + \textstyle{\frac{1}{2}} (1-j)\big).
The Inverse-Wishart distribution {\boldsymbol{X}}\sim \operatorname{InvWish}({\boldsymbol{\Psi}}, \nu) is defined as {\boldsymbol{X}}^{-1} \sim \operatorname{Wish}({\boldsymbol{\Psi}}^{-1}, \nu). Its log-density is given by
\log p({\boldsymbol{X}}\mid {\boldsymbol{\Psi}}, \nu) = -\textstyle{\frac{1}{2}} \left[\mathrm{tr}({\boldsymbol{\Psi}}{\boldsymbol{X}}^{-1}) + (\nu+q+1) \log |{\boldsymbol{X}}| - \nu \log |{\boldsymbol{\Psi}}| + \nu q \log(2) + 2 \log \Gamma_q(\textstyle{\frac{\nu }{2}})\right].
If {\boldsymbol{X}}_{q\times q} \sim \operatorname{Wish}({\boldsymbol{\Psi}},\nu), the for a nonzero vector {\boldsymbol{a}}\in \mathbb R^q we have
\frac{{\boldsymbol{a}}'{\boldsymbol{X}}{\boldsymbol{a}}}{{\boldsymbol{a}}'{\boldsymbol{\Psi}}{\boldsymbol{a}}} \sim \chi^2_{(\nu)}.
The Matrix-Normal distribution on a random matrix {\boldsymbol{X}}_{p \times q} is denoted {\boldsymbol{X}}\sim \operatorname{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C), and defined as {\boldsymbol{X}}= {\boldsymbol{L}}{\boldsymbol{Z}}{\boldsymbol{U}}+ {\boldsymbol{\Lambda}}, where:
The log-density of the Matrix-Normal distribution is
\log p({\boldsymbol{X}}\mid {\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C) = -\textstyle{\frac{1}{2}} \left[\mathrm{tr}\big({\boldsymbol{\Sigma}}_C^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}})'{\boldsymbol{\Sigma}}_R^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}})\big) + \nu q \log(2\pi) + \nu \log |{\boldsymbol{\Sigma}}_C| + q \log |{\boldsymbol{\Sigma}}_R|\right].
If {\boldsymbol{X}}_{p \times q} \sim \operatorname{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C), then for nonzero vectors {\boldsymbol{a}}\in \mathbb R^p and {\boldsymbol{b}}\in \mathbb R^q we have
{\boldsymbol{a}}' {\boldsymbol{X}}{\boldsymbol{b}}\sim \operatorname{Normal}({\boldsymbol{a}}' {\boldsymbol{\Lambda}}{\boldsymbol{b}}, {\boldsymbol{a}}'{\boldsymbol{\Sigma}}_R{\boldsymbol{a}}\cdot {\boldsymbol{b}}'{\boldsymbol{\Sigma}}_C{\boldsymbol{b}}).
The Matrix-Normal Inverse-Wishart Distribution on a random matrix {\boldsymbol{X}}_{p \times q} and random positive-definite matrix {\boldsymbol{V}}_{q\times q} is denoted ({\boldsymbol{X}},{\boldsymbol{V}}) \sim \operatorname{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}, {\boldsymbol{\Psi}}, \nu), and defined as
\begin{aligned} {\boldsymbol{X}}\mid {\boldsymbol{V}}& \sim \operatorname{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}, {\boldsymbol{V}}) \\ {\boldsymbol{V}}& \sim \operatorname{InvWish}({\boldsymbol{\Psi}}, \nu). \end{aligned}
The MNIX distribution is conjugate prior for the multivariable response regression model
{\boldsymbol{Y}}_{n \times q} \sim \operatorname{MatNorm}({\boldsymbol{X}}_{n\times p} {\boldsymbol{\beta}}_{p \times q}, {\boldsymbol{V}}, {\boldsymbol{\Sigma}}).
That is, if ({\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) \sim \operatorname{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Omega}}^{-1}, {\boldsymbol{\Psi}}, \nu), then
{\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}\mid {\boldsymbol{Y}}\sim \operatorname{MNIW}(\hat {\boldsymbol{\Lambda}}, \hat {\boldsymbol{\Omega}}^{-1}, \hat {\boldsymbol{\Psi}}, \hat \nu),
where
\begin{aligned} \hat {\boldsymbol{\Omega}}& = {\boldsymbol{X}}'{\boldsymbol{V}}^{-1}{\boldsymbol{X}}+ {\boldsymbol{\Omega}} & \hat {\boldsymbol{\Psi}}& = {\boldsymbol{\Psi}}+ {\boldsymbol{Y}}'{\boldsymbol{V}}^{-1}{\boldsymbol{Y}}+ {\boldsymbol{\Lambda}}'{\boldsymbol{\Omega}}{\boldsymbol{\Lambda}}- \hat {\boldsymbol{\Lambda}}'\hat {\boldsymbol{\Omega}}\hat {\boldsymbol{\Lambda}} \\ \hat {\boldsymbol{\Lambda}}& = \hat {\boldsymbol{\Omega}}^{-1}({\boldsymbol{X}}'{\boldsymbol{V}}^{-1}{\boldsymbol{Y}}+ {\boldsymbol{\Omega}}{\boldsymbol{\Lambda}}) & \hat \nu & = \nu + n. \end{aligned}
The Matrix-t distribution on a random matrix {\boldsymbol{X}}_{p \times q} is denoted {\boldsymbol{X}}\sim \operatorname{MatT}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu), and defined as the marginal distribution of {\boldsymbol{X}} for ({\boldsymbol{X}}, {\boldsymbol{V}}) \sim \operatorname{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu). Its log-density is given by
\begin{aligned} \log p({\boldsymbol{X}}\mid {\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu) & = -\textstyle{\frac{1}{2}} \Big[(\nu+p+q-1)\log | I + {\boldsymbol{\Sigma}}_R^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}}){\boldsymbol{\Sigma}}_C^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}})'| \\ & \phantom{= -\textstyle{\frac{1}{2}} \Big[} + q \log |{\boldsymbol{\Sigma}}_R| + p \log |{\boldsymbol{\Sigma}}_C| \\ & \phantom{= -\textstyle{\frac{1}{2}} \Big[} + pq \log(\pi) - \log \Gamma_q(\textstyle{\frac{\nu+p+q-1}{2}}) + \log \Gamma_q(\textstyle{\frac{\nu+q-1}{2}})\Big]. \end{aligned}
If {\boldsymbol{X}}_{p\times q} \sim \operatorname{MatT}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu), then for nonzero vectors {\boldsymbol{a}}\in \mathbb R^p and {\boldsymbol{b}}\in \mathbb R^q we have
\frac{{\boldsymbol{a}}'{\boldsymbol{X}}{\boldsymbol{b}}- \mu}{\sigma} \sim t_{(\nu -q + 1)},
where \mu = {\boldsymbol{a}}'{\boldsymbol{\Lambda}}{\boldsymbol{b}}, \qquad \sigma^2 = \frac{{\boldsymbol{a}}'{\boldsymbol{\Sigma}}_R{\boldsymbol{a}}\cdot {\boldsymbol{b}}'{\boldsymbol{\Sigma}}_C{\boldsymbol{b}}}{\nu - q + 1}.
Consider the multivariate normal distribution on q-dimensional vectors {\boldsymbol{x}} and {\boldsymbol{\mu}} given by
\begin{aligned} {\boldsymbol{x}}\mid {\boldsymbol{\mu}}& \sim \operatorname{Normal}({\boldsymbol{\mu}}, {\boldsymbol{V}}) \\ {\boldsymbol{\mu}}& \sim \operatorname{Normal}({\boldsymbol{\lambda}}, {\boldsymbol{\Sigma}}). \end{aligned}
The random-effects normal distribution is defined as the posterior distribution {\boldsymbol{\mu}}\sim p({\boldsymbol{\mu}}\mid {\boldsymbol{x}}), which is given by
{\boldsymbol{\mu}}\mid {\boldsymbol{x}}\sim \operatorname{Normal}\big({\boldsymbol{G}}({\boldsymbol{x}}-{\boldsymbol{\lambda}}) + {\boldsymbol{\lambda}}, {\boldsymbol{G}}{\boldsymbol{V}}\big), \qquad {\boldsymbol{G}}= {\boldsymbol{\Sigma}}({\boldsymbol{V}}+ {\boldsymbol{\Sigma}})^{-1}.
The notation for this distribution is {\boldsymbol{\mu}}\sim \operatorname{RxNorm}({\boldsymbol{x}}, {\boldsymbol{V}}, {\boldsymbol{\lambda}}, {\boldsymbol{\Sigma}}).
The hierarchical Normal-Normal model is defined as
\begin{aligned} {\boldsymbol{y}}_i \mid {\boldsymbol{\mu}}_i, {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}& \overset{\:\textrm{ind}\:}{\sim}\operatorname{Normal}({\boldsymbol{\mu}}_i, {\boldsymbol{V}}_i) \\ {\boldsymbol{\mu}}_i \mid {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}& \overset{\;\textrm{iid}\;}{\sim}\operatorname{Normal}({\boldsymbol{x}}_i'{\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) \\ ({\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) & \sim \operatorname{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Omega}}^{-1}, {\boldsymbol{\Psi}}, \nu), \end{aligned}
where:
Let {\boldsymbol{Y}}_{n\times q} = ({\boldsymbol{y}}_{1},\ldots,{\boldsymbol{y}}_{n}), {\boldsymbol{X}}_{n\times p} = ({\boldsymbol{x}}_{1},\ldots,{\boldsymbol{x}}_{n}), and {\boldsymbol{\Theta}}_{n \times q} = ({\boldsymbol{\mu}}_{1},\ldots,{\boldsymbol{\mu}}_{n}). If interest lies in the posterior distribution p({\boldsymbol{\Theta}}, {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}\mid {\boldsymbol{Y}}, {\boldsymbol{X}}), then a Gibbs sampler can be used to cycle through the following conditional distributions:
\begin{aligned} {\boldsymbol{\mu}}_i \mid {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}, {\boldsymbol{Y}}, {\boldsymbol{X}}& \overset{\:\textrm{ind}\:}{\sim}\operatorname{RxNorm}({\boldsymbol{y}}_i, {\boldsymbol{V}}_i, {\boldsymbol{x}}_i'{\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) \\ {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}\mid {\boldsymbol{\Theta}}, {\boldsymbol{Y}}, {\boldsymbol{X}}& \sim \operatorname{MNIW}(\hat {\boldsymbol{\Lambda}}, \hat {\boldsymbol{\Omega}}^{-1}, \hat {\boldsymbol{\Psi}}, \hat \nu), \end{aligned}
where \hat {\boldsymbol{\Lambda}}, \hat {\boldsymbol{\Omega}}, \hat {\boldsymbol{\Psi}}, and \hat \nu are obtained from the MNIW conjugate posterior formula with {\boldsymbol{Y}}\gets {\boldsymbol{\Theta}}.