---
title: "Matrix variate Beta distributions"
author: "Stéphane Laurent"
date: "2017-12-12"
output:
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  md_document:
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tags: R, maths, statistics
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---

$\newcommand{\etr}{\textrm{etr}}$

One asked me what are the densities of the matrix variate Beta distributions 
in the [`matrixsampling` package](https://github.com/stla/matrixsampling). 
I'm going to give them here.


# Definitions

Two definitions of the matrix variate Beta type I distribution were proposed. 
We will denote them by $\mathcal{B}I_p^{(1)}(a,b,\Theta_1,\Theta_2)$ and 
$\mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)$, where $\Theta_1$ and $\Theta_2$ are the 
noncentrality parameters. 
Take two independent Wishart random matrices 
$W_1 \sim \mathcal{W}_p(2a, I_p, \Theta_1)$ 
and $W_2 \sim \mathcal{W}_p(2b, I_p, \Theta_2)$.
Then $\mathcal{B}I_p^{(1)}(a,b,\Theta_1,\Theta_2)$ is the distribution of 
$$
U_1 = {(W_1+W_2)}^{-\frac12}W_1{(W_1+W_2)}^{-\frac12},
$$
while $\mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)$ is the distribution of 
$$
U_2 = W_1^\frac12{(W_1+W_2)}^{-1}W_1^\frac12.
$$
The condition $a+b > \frac12(p-1)$ is required in order for $W_1+W_2$ to be invertible.

In the central case, i.e. when both $\Theta_1$ and $\Theta_2$ are the null matrices, 
these two distributions are the same. 
More generally, as we will see, 
they are the same when $\Theta_1$ and $\Theta_2$ are scalar.

Similarly, two definitions of the matrix variate Beta type II distribution were proposed. 
We will denote them by $\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)$ and 
$\mathcal{B}II_p^{(2)}(a,b,\Theta_1,\Theta_2)$. 
The first one is the distribution of 
$$
V_1 = W_2^{-\frac12} W_1 W_2^{-\frac12},
$$
while the second one is the distribution of 
$$
V_2 = W_1^\frac12 {W_2}^{-1} W_1^\frac12.
$$
The condition $b > \frac12(p-1)$ is required in order for $W_2$ to be invertible.

Similarly to the type I, these two distributions are the same in the central case, 
and more generally when $\Theta_1$ and $\Theta_2$ are scalar.

Under the second definition, the Beta type I distribution is related to the Beta type II distribution by $U_2 \sim V_2{(I_p+V_2)}^{-1}$.


# Hypergeometric matrix function

The densities of the matrix Beta distributions involve the hypergeometric function 
of matrix argument ${}_0\!F_1$. 
We will use the property ${}_0\!F_1(\alpha, AB)={}_0\!F_1(\alpha, BA)$ 
(to simplify the densities when $\Theta_1$ or $\Theta_2$ are scalar).


# Densities and identities

We provide the densities here. 
We will prove these results in the next section. 
Note that $a$ and $b$ must satisfy $a,b > \frac12(p+1)$ in order 
for each distribution to have a density.

We denote by $\etr$ the function taking the exponential of the trace of a matrix.

### $\boxed{\mathcal{B}I_p^{(1)}(a, b, \Theta_1, \Theta_2)}$

Recall that 
$$
U_1 = {(W_1+W_2)}^{-\frac12} W_1 {(W_1+W_2)}^{-\frac12}.
$$
It is clear from this definition that
$$
I_p - U_1 \sim \mathcal{B}I_p^{(1)}(b, a, \Theta_2, \Theta_1).
$$
The density of $U_1$ is 
$$
\begin{split}
& \mathcal{B}I_p^{(1)}(U \mid a, b, \Theta_1, \Theta_2) \propto 
{\det(U)}^{a-\frac12(p+1)} {\det(I_p-U)}^{b-\frac12(p+1)} \\
& \quad \int_{S>0} \etr(-S)
{\det(S)}^{a+b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S^{\frac12} U S^\frac12\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S^{\frac12}(I_p-U)S^\frac12\right)
\mathrm{d}S
\end{split}
$$
for $0 < U < I_p$.

If $\Theta_1$ and $\Theta_2$ are scalar,
$$
\begin{split}
& \mathcal{B}I_p^{(1)}(U \mid a, b, \Theta_1, \Theta_2) \propto
{\det(U)}^{a-\frac12(p+1)} {\det(I_p-U)}^{b-\frac12(p+1)} \\
& \quad \int_{S>0} \etr(-S)
{\det(S)}^{a+b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1SU\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S(I_p-U)\right)
\mathrm{d}S.
\end{split}
$$


### $\boxed{\mathcal{B}I_p^{(2)}(a, b, \Theta_1, \Theta_2)}$

Recall that
$$
U_2 = W_1^\frac12{(W_1+W_2)}^{-1}W_1^\frac12.
$$

The density of $U_2$ is 
$$
\begin{split}
& \mathcal{B}I_p^{(2)}(U \mid a, b, \Theta_1, \Theta_2) \propto  
 {\det(U)}^{-b-\frac12(p+1)}
{\det(I_p-U)}^{b-\frac12(p+1)} \\
& \quad \int_{S>0}
\etr(-U^{-1}S)
{\det(S)}^{a+b-\frac12(p+1)} 
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2 S^\frac12 U^{-1}(I_p-U)S^{\frac12}\right)\mathrm{d}S
\end{split}
$$
for $0 < U < I_p$.

If $\Theta_1$ and $\Theta_2$ are scalar, it is equal to the density of 
$\mathcal{B}I_p^{(1)}(a, b, \Theta_1, \Theta_2)$.


### $\boxed{\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)}$

More generally, we give the density of
$$
V_1 = {(W_2^{-\frac12})}' W_1 W_2^{-\frac12}
$$
where $W_2^{\frac12}{(W_2^{\frac12})}' = W_2$.

This density of $V_1$ is 
$$
\begin{split}
& \mathcal{B}II_p^{(1)}(V \mid a, b, \Theta_1, \Theta_2) \propto 
 {\det(V)}^{a-\frac12(p+1)} \\
& \quad
\int_{S>0}
\etr\bigl(-(I_p+V)S\bigr)
{\det(S)}^{a+b-\frac12(p+1)} 
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1{(S^{\frac12})}' V S^{\frac12}\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S\right) \mathrm{d}S
\end{split}
$$
for $V>0$. 

If $\Theta_1$ is scalar, the distribution does not depend on the choice of $W_2^\frac12$. 


### $\boxed{\mathcal{B}II_p^{(2)}(a,b,\Theta_1,\Theta_2)}$

More generally, we give the density of 
$$
V_2 = W_1^{\frac12} W_2^{-1} {(W_1^{\frac12})}'
$$
where $W_1^{\frac12}{(W_1^{\frac12})}' = W_1$.

Assuming $a > \frac12(p-1)$, it is clear from the definitions that
$$
V_2^{-1} \sim \mathcal{B}II_p^{(1)}(b,a,\Theta_2,\Theta_1).
$$

The density of $V_2$ is 
$$
\begin{split}
& \mathcal{B}II_p^{(2)}(V \mid a, b, \Theta_1, \Theta_2) \propto  
 {\det(V)}^{-b-\frac12(p+1)} \\ 
& \quad \int_{S >0}
\etr\bigl(-(I_p+V^{-1})S\bigr)
{\det(S)}^{a+b-\frac12(p+1)}  
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2{(S^\frac12)}' V^{-1} S^\frac12\right)
\mathrm{d}S.
\end{split}
$$
for $V >0$. 

If $\Theta_2$ is scalar, the distribution does not depend on the choice of $W_1^\frac12$. 

If $\Theta_1$ and $\Theta_2$ are scalar, this is the same distribution as 
$\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)$.

If we take $W_1^{\frac12}$ the symmetric square root of $W_1$, then
$V_2{(I_p+V_2)}^{-1} \sim \mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)$.


# Proofs

To derive the densities, we start with the joint density of 
$W_1$ and $W_2$ which is
$$
C \, \etr\left(-\frac12 W_1\right)\etr\left(-\frac12 W_2\right)
{\det(W_1)}^{a-\frac12(p+1)} {\det(W_2)}^{b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1W_1\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2W_2\right)
$$
where $C$ is a constant.

In our following calculations, we always denote by $C$ a constant not 
depending of the variables. 
The value of $C$ is relative to each expression in which it is contained: 
two $C$'s written at two different places have not the same value.


### $\boxed{\mathcal{B}I_p^{(1)}(a,b,\Theta_1,\Theta_2)}$

Recall that $\mathcal{B}I_p^{(1)}(a,b,\Theta_1,\Theta_2)$ is the 
distribution of 
$$
U = {(W_1+W_2)}^{-\frac12}W_1{(W_1+W_2)}^{-\frac12}.
$$
By applying the transformation $W_1+W_2=S$ and $W_1 = S^{\frac12} U S^\frac12$, 
whose Jacobian is $J(W_1, W_2 \rightarrow U, S) = {\det(S)}^{\frac12(p+1)}$, we get 
the pdf of $(U,S)$ as 
$$
\begin{aligned}
C\, &
{\det(U)}^{a-\frac12(p+1)} {\det(I_p-U)}^{b-\frac12(p+1)} \\ 
& 
\etr\left(-\frac12 S\right)
 {\det(S)}^{a+b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1S^{\frac12} U S^\frac12\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2S^{\frac12}(I_p-U) S^\frac12\right).
\end{aligned}
$$
Thus the density of $U$ is 
$$
\begin{aligned}
C\, &
{\det(U)}^{a-\frac12(p+1)} {\det(I_p-U)}^{b-\frac12(p+1)} \\
& \int_{S>0} \etr\left(-\frac12 S\right)
 {\det(S)}^{a+b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1S^{\frac12}U{(S^\frac12)}'\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2S^{\frac12}(I_p-U)S^\frac12\right) 
\mathrm{d}S
\\ 
= C\,& 
{\det(U)}^{a-\frac12(p+1)} {\det(I_p-U)}^{b-\frac12(p+1)} \\
& \int_{S>0} \etr(-S)
{\det(S)}^{a+b-\frac12(p+1)}
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S^{\frac12} U S^\frac12\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S^{\frac12}(I_p-U)S^\frac12\right)
\mathrm{d}S. 
\end{aligned}
$$

### $\boxed{\mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)}$

Recall that $\mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)$ is the 
distribution of 
$$
U = W_1^\frac12{(W_1+W_2)}^{-1}W_1^\frac12.
$$

We use the transformation $W_1+W_2 = W_1^{\frac12}U^{-1}W_1^{\frac12}$.  The Jacobian of this transformation is $J(W_1, W_2 \rightarrow U, W_1) = {\det(W_1)}^{\frac12(p+1)} {\det(U)}^{-(p+1)}$, and 
we get the pdf of $(U,W_1)$ as 
$$
\begin{aligned}
C \, &
{\det(W_1)}^{a+b-\frac12(p+1)} 
{\det(U)}^{-(p+1)}
{\det(U^{-1}-I_p)}^{b-\frac12(p+1)} \\
& \etr\left(-\frac12 W_1U^{-1}\right)
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1W_1\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2W_1^{\frac12}(U^{-1}-I_p)W_1^\frac12\right) 
\\ = 
C \, & 
{\det(W_1)}^{a+b-\frac12(p+1)} 
{\det(U)}^{-b-\frac12(p+1)}
{\det(I_p-U)}^{b-\frac12(p+1)} \\ 
& \etr\left(-\frac12 W_1U^{-1}\right)
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1W_1\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2W_1^{\frac12}U^{-1}(I_p-U)W_1^\frac12\right).
\end{aligned}
$$
Thus the density of $U$ is 
$$
\begin{aligned}
C\, &
{\det(U)}^{-b-\frac12(p+1)}
{\det(I_p-U)}^{b-\frac12(p+1)} \\ 
& \int_{W_1>0}
\etr\left(-\frac12 W_1U^{-1}\right)
{\det(W_1)}^{a+b-\frac12(p+1)} \\
& \qquad
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1W_1\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2W_1^{\frac12}U^{-1}(I_p-U)W_1^\frac12\right)
\mathrm{d}W_1\\
= C\, &  
{\det\bigl(U{(I_p-U)}^{-1}\bigr)}^{-b-\frac12(p+1)}
{\det(I_p-U)}^{-(p+1)} \\
& \int_{S>0}
\etr(- U^{-1}S)
{\det(S)}^{a+b-\frac12(p+1)} 
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S^{\frac12}U^{-1}(I_p-U)S^\frac12\right)
\mathrm{d}S.
\end{aligned}
$$
Let's derive the density of $U{(I_p-U)}^{-1}$. 
Using the transformation $U = V{(I_p+V)}^{-1}$ with Jacobian 
$J(U \rightarrow V) = {\det(I_p+V)}^{-(p+1)}$, we get the density of $V$ as
$$
\begin{aligned}
C\, &
{\det(V)}^{-b-\frac12(p+1)} \\
& \int_{S>0}
\etr\bigl(-(I_p+V^{-1})S\bigr)
{\det(S)}^{a+b-\frac12(p+1)} 
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S^{\frac12}V^{-1}S^\frac12\right)
\mathrm{d}S.
\end{aligned}
$$
This is the density of $\mathcal{B}II_p^{(2)}(a,b,\Theta_1,\Theta_2)$.


### $\boxed{\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)}$

Recall that $\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)$ 
is the distribution of 
$$
V = {(W_2^{-\frac12})}' W_1 W_2^{-\frac12}.
$$

Transforming $W_1 = {(W_2^{\frac12})}' V W_2^{\frac12}$ with Jacobian 
$J(W_1, W_2 \rightarrow V, W_2) = {\det(W_2)}^{\frac12(p+1)}$, we get 
the density of $(V,W_2)$ as
$$
\begin{aligned}
C\, & 
{\det(V)}^{a-\frac12(p+1)} {\det(W_2)}^{a+b-\frac12(p+1)} \\
& 
\etr\left(-\frac12 (I_p+V)W_2\right)
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1 {(W_2^{\frac12})}' V W_2^{\frac12}\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2W_2\right).
\end{aligned}
$$
Thus, the density of $V$ is 
$$
\begin{aligned}
C\, & 
{\det(V)}^{a-\frac12(p+1)} \\ 
& \int_{S>0}
\etr\bigl(-(I_p+V)S\bigr)
{\det(S)}^{a+b-\frac12(p+1)} 
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1{(S^{\frac12})}' V S^{\frac12}\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2S\right)
\mathrm{d}S.
\end{aligned}
$$


### $\boxed{\mathcal{B}II_p^{(2)}(a,b,\Theta_1,\Theta_2)}$

Recall that $\mathcal{B}II_p^{(2)}(a,b,\Theta_1,\Theta_2)$ 
is the distribution of 
$$
V = W_1^{\frac12} W_2^{-1} {(W_1^{\frac12})}'.
$$

We apply the transformation $W_2 = {(W_1^\frac12)}' V^{-1} W_1^\frac12$.   The Jacobian of this transformation is 
$J(W_1, W_2 \rightarrow V, W_1) = {\det(W_1)}^{\frac12(p+1)}{\det(V)}^{-(p+1)}$. 
We get the density of $(V,W_1)$ as
$$
\begin{aligned}
C\, &
{\det(W_1)}^{a+b-\frac12(p+1)} {\det(V)}^{-b-\frac12(p+1)} \\
&
\etr\left(-\frac12 W_1\right)\etr\left(-\frac12 W_1V^{-1}\right)
{}_0\!F_1\left(a, \frac{1}{4}\Theta_1W_1\right)
{}_0\!F_1\left(b, \frac{1}{4}\Theta_2{(W_1^\frac12)}' V^{-1} W_1^\frac12\right).
\end{aligned}
$$
Thus the density of $V$ is 
$$
\begin{aligned}
C\, &
{\det(V)}^{-b-\frac12(p+1)} \\ 
& \int_{S >0}
\etr\bigl(-(I_p+V^{-1})S\bigr)
{\det(S)}^{a+b-\frac12(p+1)}  
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1S\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2{(S^\frac12)}' V^{-1} S^\frac12\right)
\mathrm{d}S.
\end{aligned}
$$
Now assume $\Theta_2$ is scalar. Then one has 
$$
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2{(S^\frac12)}' V^{-1} S^\frac12\right) 
= {}_0\!F_1\left(b, \frac{1}{2}\Theta_2 S V^{-1}\right).
$$
Doing the change of variables $R = S V^{-1}$ in the integral, we get
the density of $V$ as
$$
\begin{aligned}
C\, &
{\det(V)}^{a-\frac12(p+1)} \\ 
& \int_{R >0}
\etr\bigl(-(I_p+V)R\bigr)
{\det(R)}^{a+b-\frac12(p+1)}  
{}_0\!F_1\left(a, \frac{1}{2}\Theta_1 R V\right)
{}_0\!F_1\left(b, \frac{1}{2}\Theta_2 R\right)
\mathrm{d}R.
\end{aligned}
$$
If in addition, $\Theta_1$ is scalar, this is the density of 
$\mathcal{B}II_p^{(1)}(a,b,\Theta_1,\Theta_2)$.


## Simplifications in the singly noncentral case

One has ${}_0\!F_1(\alpha, \mathbf{0}) = 1$. 
Thus, when $\Theta_1$ or $\Theta_2$ is the null matrix, we get integrals like
$$
\int_{S>0}
\etr(-ZS)
{\det(S)}^{\alpha-\frac12(p+1)} 
{}_0\!F_1\left(\beta, \frac{1}{2}\Theta S T \right)
\mathrm{d}S,
$$
for example in the density of $\mathcal{B}I_p^{(2)}(a,b,\Theta_1,\Theta_2)$ when $\Theta_2$ is the null matrix, or in the density of 
$\mathcal{B}I_p^{(1)}(a,b,\Theta_1,\Theta_2)$ when $\Theta_1$ is the null matrix and 
$\Theta_2$ is scalar.

This integral is equal to
$$
\Gamma_p(\alpha){\det(Z)}^{-\alpha} 
{}_1\!F_1\left(\alpha, \beta, \frac{1}{2}\Theta Z^{-1} T \right).
$$
