In statistics, the matrix variate Dirichlet distribution is a generalization of the matrix variate beta distribution and of the Dirichlet distribution.
Suppose are positive definite matrices with also positive-definite, where is the identity matrix. Then we say that the have a matrix variate Dirichlet distribution, , if their joint probability density function is
where and is the multivariate beta function.
If we write then the PDF takes the simpler form
on the understanding that .
Theorems
generalization of chi square-Dirichlet result
Suppose are independently distributed Wishart positive definite matrices. Then, defining (where is the sum of the matrices and is any reasonable factorization of ), we have
Marginal distribution
If , and if , then:
Conditional distribution
Also, with the same notation as above, the density of is given by
where we write .
partitioned distribution
Suppose and suppose that is a partition of (that is, and if ). Then, writing and (with ), we have:
partitions
Suppose . Define
where is and is . Writing the Schur complement we have
and
See also
References
A. K. Gupta and D. K. Nagar 1999. "Matrix variate distributions". Chapman and Hall.