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.


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