Distributions Provided by the mniw Package

Wishart Distribution

The Wishart distribution on a random positive-definite matrix $${\boldsymbol{X}}_{q\times q}$$ is is denoted $${\boldsymbol{X}}\sim \textrm{Wish}({\boldsymbol{\Psi}}, \nu)$$, 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

$Z_{ij} \begin{cases} \overset{\;\textrm{iid}\;}{\sim}\mathcal{N}(0,1) & i < j \\ \overset{\:\textrm{ind}\:}{\sim}\chi^2_{(\nu-i+1)} & i = j \\ = 0 & i > j. \end{cases}$

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 $\Gamma_n(x) = \pi^{n(n-1)/4} \prod_{j=1}^n \Gamma\big(x + \textstyle{\frac{1}{2}} (1-j)\big).$

Inverse-Wishart Distribution

The Inverse-Wishart distribution $${\boldsymbol{X}}\sim \textrm{InvWish}({\boldsymbol{\Psi}}, \nu)$$ is defined as $${\boldsymbol{X}}^{-1} \sim \textrm{Wish}({\boldsymbol{\Psi}}^{-1}, \nu)$$. Its log-density is given by $\log p({\boldsymbol{X}}\mid {\boldsymbol{\Psi}}, \nu) = -\textstyle{\frac{1}{2}} \left[\mathrm{tr}({\boldsymbol{\Psi}}{\boldsymbol{X}}^{-1}) + (\nu+q+1) \log |{\boldsymbol{X}}| - \nu \log |{\boldsymbol{\Psi}}| + \nu q \log(2) + 2 \log \Gamma_q(\textstyle{\frac{\nu }{2}})\right].$

Properties

If $${\boldsymbol{X}}_{q\times q} \sim \textrm{Wish}({\boldsymbol{\Psi}},\nu)$$, the for a nonzero vector $${\boldsymbol{a}}\in \mathbb R^q$$ we have $\frac{{\boldsymbol{a}}'{\boldsymbol{X}}{\boldsymbol{a}}}{{\boldsymbol{a}}'{\boldsymbol{\Psi}}{\boldsymbol{a}}} \sim \chi^2_{(\nu)}.$

Matrix-Normal Distribution

The Matrix-Normal distribution on a random matrix $${\boldsymbol{X}}_{p \times q}$$ is denoted $${\boldsymbol{X}}\sim \textrm{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C)$$, and defined as $${\boldsymbol{X}}= {\boldsymbol{L}}{\boldsymbol{Z}}{\boldsymbol{U}}+ {\boldsymbol{\Lambda}}$$, where:

• $${\boldsymbol{\Lambda}}_{p \times q}$$ is the mean matrix parameter,
• $${{\boldsymbol{\Sigma}}_R}_{p \times p} = {\boldsymbol{L}}{\boldsymbol{L}}'$$ is the row-variance matrix parameter,
• $${{\boldsymbol{\Sigma}}_C}_{q \times q} = {\boldsymbol{U}}'{\boldsymbol{U}}$$ is the column-variance matrix parameter,
• $${\boldsymbol{Z}}_{q\times q}$$ is a random matrix with $$Z_{ij} \overset{\;\textrm{iid}\;}{\sim}\mathcal{N}(0,1)$$.

The log-density of the Matrix-Normal distribution is $\log p({\boldsymbol{X}}\mid {\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C) = -\textstyle{\frac{1}{2}} \left[\mathrm{tr}\big({\boldsymbol{\Sigma}}_C^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}})'{\boldsymbol{\Sigma}}_R^{-1}({\boldsymbol{X}}-{\boldsymbol{\Lambda}})\big) + \nu q \log(2\pi) + \nu \log |{\boldsymbol{\Sigma}}_C| + q \log |{\boldsymbol{\Sigma}}_R|\right].$

Properties

If $${\boldsymbol{X}}_{p \times q} \sim \textrm{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C)$$, then for nonzero vectors $${\boldsymbol{a}}\in \mathbb R^p$$ and $${\boldsymbol{b}}\in \mathbb R^q$$ we have ${\boldsymbol{a}}' {\boldsymbol{X}}{\boldsymbol{b}}\sim \mathcal{N}({\boldsymbol{a}}' {\boldsymbol{\Lambda}}{\boldsymbol{b}}, {\boldsymbol{a}}'{\boldsymbol{\Sigma}}_R{\boldsymbol{a}}\cdot {\boldsymbol{b}}'{\boldsymbol{\Sigma}}_C{\boldsymbol{b}}).$

Matrix-Normal Inverse-Wishart Distribution

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 $$({\boldsymbol{X}},{\boldsymbol{V}}) \sim \textrm{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}, {\boldsymbol{\Psi}}, \nu)$$, and defined as \begin{aligned} {\boldsymbol{X}}\mid {\boldsymbol{V}}& \sim \textrm{MatNorm}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}, {\boldsymbol{V}}) \\ {\boldsymbol{V}}& \sim \textrm{InvWish}({\boldsymbol{\Psi}}, \nu). \end{aligned}

Properties

The MNIX distribution is conjugate prior for the multivariable response regression model ${\boldsymbol{Y}}_{n \times q} \sim \textrm{MatNorm}({\boldsymbol{X}}_{n\times p} {\boldsymbol{\beta}}_{p \times q}, {\boldsymbol{V}}, {\boldsymbol{\Sigma}}).$ That is, if $$({\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) \sim \textrm{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Omega}}^{-1}, {\boldsymbol{\Psi}}, \nu)$$, then ${\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}\mid {\boldsymbol{Y}}\sim \textrm{MNIW}(\hat {\boldsymbol{\Lambda}}, \hat {\boldsymbol{\Omega}}^{-1}, \hat {\boldsymbol{\Psi}}, \hat \nu),$ 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}

Matrix-t Distribution

The Matrix-$$t$$ distribution on a random matrix $${\boldsymbol{X}}_{p \times q}$$ is denoted $${\boldsymbol{X}}\sim \textrm{MatT}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu)$$, and defined as the marginal distribution of $${\boldsymbol{X}}$$ for $$({\boldsymbol{X}}, {\boldsymbol{V}}) \sim \textrm{MNIW}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu)$$. 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}

Properties

If $${\boldsymbol{X}}_{p\times q} \sim \textrm{MatT}({\boldsymbol{\Lambda}}, {\boldsymbol{\Sigma}}_R, {\boldsymbol{\Sigma}}_C, \nu)$$, 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 $\mu = {\boldsymbol{a}}'{\boldsymbol{\Lambda}}{\boldsymbol{b}}, \qquad \sigma^2 = \frac{{\boldsymbol{a}}'{\boldsymbol{\Sigma}}_R{\boldsymbol{a}}\cdot {\boldsymbol{b}}'{\boldsymbol{\Sigma}}_C{\boldsymbol{b}}}{\nu - q + 1}.$

Random-Effects Normal Distribution

Consider the multivariate normal distribution on $$q$$-dimensional vectors $${\boldsymbol{x}}$$ and $${\boldsymbol{\mu}}$$ given by \begin{aligned} {\boldsymbol{x}}\mid {\boldsymbol{\mu}}& \sim \mathcal{N}({\boldsymbol{\mu}}, {\boldsymbol{V}}) \\ {\boldsymbol{\mu}}& \sim \mathcal{N}({\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 ${\boldsymbol{\mu}}\mid {\boldsymbol{x}}\sim \mathcal{N}\big({\boldsymbol{G}}({\boldsymbol{x}}-{\boldsymbol{\lambda}}) + {\boldsymbol{\lambda}}, {\boldsymbol{G}}{\boldsymbol{V}}\big), \qquad {\boldsymbol{G}}= {\boldsymbol{\Sigma}}({\boldsymbol{V}}+ {\boldsymbol{\Sigma}})^{-1}.$ The notation for this distribution is $${\boldsymbol{\mu}}\sim \textrm{RxNorm}({\boldsymbol{x}}, {\boldsymbol{V}}, {\boldsymbol{\lambda}}, {\boldsymbol{\Sigma}})$$.

Hierarchical Normal-Normal Model

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}\mathcal{N}({\boldsymbol{\mu}}_i, {\boldsymbol{V}}_i) \\ {\boldsymbol{\mu}}_i \mid {\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}& \overset{\;\textrm{iid}\;}{\sim}\mathcal{N}({\boldsymbol{x}}_i'{\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) \\ ({\boldsymbol{\beta}}, {\boldsymbol{\Sigma}}) & \sim \textrm{MNIW}({\boldsymbol{\Lambda}}, \Omega^{-1}, {\boldsymbol{\Psi}}, \nu), \end{aligned} where:

• $${{\boldsymbol{y}}_i}_{q\times 1}$$ is the response vector for subject $$i$$,
• $${{\boldsymbol{\mu}}_i}_{q\times 1}$$ is the random effect for subject $$i$$,
• $${{\boldsymbol{V}}_i}_{q\times q}$$ is the error variance for subject $$i$$,
• $${{\boldsymbol{x}}_i}_{p\times 1}$$ is the covariate vector for subject $$i$$,
• $${\boldsymbol{\beta}}_{p \times q}$$ is the random-effects coefficient matrix,
• $${\boldsymbol{\Sigma}}_{q \times q}$$ is the random-effects error variance.

Let $${\boldsymbol{Y}}_{n\times q} = (n_{\boldsymbol{y}},\ldots,n_{)}$$, $${\boldsymbol{X}}_{n\times p} = (n_{\boldsymbol{x}},\ldots,n_{)}$$, and $${\boldsymbol{\Theta}}_{n \times q} = (n_{\boldsymbol{\mu}},\ldots,n_{)}$$. 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}\textrm{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 \textrm{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 $${\boldsymbol{Y}}\gets {\boldsymbol{\Theta}}$$.