Matrix variate beta distribution

In statistics, the matrix variate beta distribution is a generalization of the beta distribution. If $U$ is a $p\times p$ positive definite matrix with a matrix variate beta distribution, and $a,b>(p-1)/2$ are real parameters, we write $U\sim B_{p}\left(a,b\right)$ (sometimes $B_{p}^{I}\left(a,b\right)$). The probability density function for $U$ is:

$\left\{\beta _{p}\left(a,b\right)\right\}^{-1}\det \left(U\right)^{a-(p+1)/2}\det \left(I_{p}-U\right)^{b-(p+1)/2}.$

Here $\beta _{p}\left(a,b\right)$ is the multivariate beta function:

$\beta _{p}\left(a,b\right)={\frac {\Gamma _{p}\left(a\right)\Gamma _{p}\left(b\right)}{\Gamma _{p}\left(a+b\right)}}$

where $\Gamma _{p}\left(a\right)$ is the multivariate gamma function given by

$\Gamma _{p}\left(a\right)=\pi ^{p(p-1)/4}\prod _{i=1}^{p}\Gamma \left(a-(i-1)/2\right).$

References

A. K. Gupta and D. K. Nagar 1999. "Matrix variate distributions". Chapman and Hall.