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In mathematics, the multivariate Gamma function, Γp(·), is a generalization of the Gamma function. It is useful in multivariate statistics, appearing in the probability density function of the Wishart and Inverse Wishart distributions.
It has two equivalent definitions. One is

where S>0 means S is positive-definite. The other one, more useful in practice, is
![{\displaystyle \Gamma _{p}(a)=\pi ^{p(p-1)/4}\prod _{j=1}^{p}\Gamma \left[a+(1-j)/2\right].}](/media/api/rest_v1/media/math/render/svg/4dc9bcc58164ece0de7aef7b0ba2b1a0925a1a04)
From this, we have the recursive relationships:
![{\displaystyle \Gamma _{p}(a)=\pi ^{(p-1)/2}\Gamma (a)\Gamma _{p-1}(a-{\tfrac {1}{2}})=\pi ^{(p-1)/2}\Gamma _{p-1}(a)\Gamma [a+(1-p)/2]}](/media/api/rest_v1/media/math/render/svg/0478e78d8d2bc4658e9682465cf5c2b0375a2416)
Thus



and so on.
Derivatives
We may define the multivariate digamma function as

and the general polygamma function as

Calculation steps

- it follows that


- it follows that

References