Multivariate t-distribution

Multivariate t
Notation	${\displaystyle t_{p}({\boldsymbol {\mu }},{\boldsymbol {\Sigma }},\nu )}$
Parameters	${\displaystyle {\boldsymbol {\mu }}=[\mu _{1},\dots ,\mu _{p}]^{T}}$ location (real ${\displaystyle p\times 1}$ vector) ${\displaystyle {\boldsymbol {\Sigma }}}$ scale matrix (positive-definite real ${\displaystyle p\times p}$ matrix) ${\displaystyle \nu >0}$ (real) represents the degrees of freedom
Support	${\displaystyle \mathbf {x} \in \mathbb {R} ^{p}\!}$
PDF	${\displaystyle {\frac {\Gamma \left[(\nu +p)/2\right]}{\Gamma (\nu /2)\nu ^{p/2}\pi ^{p/2}\left|{\boldsymbol {\Sigma }}\right|^{1/2}}}\left[1+{\frac {1}{\nu }}({\mathbf {x} }-{\boldsymbol {\mu }})^{\rm {T}}{\boldsymbol {\Sigma }}^{-1}({\mathbf {x} }-{\boldsymbol {\mu }})\right]^{-(\nu +p)/2}}$
CDF	No analytic expression, but see text for approximations
Mean	${\displaystyle {\boldsymbol {\mu }}}$ if ${\displaystyle \nu >1}$; else undefined
Median	${\displaystyle {\boldsymbol {\mu }}}$
Mode	${\displaystyle {\boldsymbol {\mu }}}$
Variance	${\displaystyle {\frac {\nu }{\nu -2}}{\boldsymbol {\Sigma }}}$ (covariance matrix) if ${\displaystyle \nu >2}$; else undefined
Skewness	0 if ${\displaystyle \nu >3}$; else undefined

In statistics, the multivariate t-distribution (or multivariate Student distribution) is a multivariate probability distribution. It is a generalization to random vectors of the Student's t-distribution, which is a distribution applicable to univariate random variables. While the case of a random matrix could be treated within this structure, the matrix t-distribution is distinct and makes particular use of the matrix structure.