In statistics, the matrix variate beta distribution is a generalization of the beta distribution. If U {\displaystyle U} is a p × p {\displaystyle p\times p} positive definite matrix with a matrix variate beta distribution, and a , b > ( p − 1 ) / 2 {\displaystyle a,b>(p-1)/2} are real parameters, we write U ∼ B p ( a , b ) {\displaystyle U\sim B_{p}\left(a,b\right)} (sometimes B p I ( a , b ) {\displaystyle B_{p}^{I}\left(a,b\right)} ). The probability density function for U {\displaystyle U} is:
Here β p ( a , b ) {\displaystyle \beta _{p}\left(a,b\right)} is the multivariate beta function:
where Γ p ( a ) {\displaystyle \Gamma _{p}\left(a\right)} is the multivariate gamma function given by