Inverse-Wishart distribution
= Inverse-Wishart distribution
{title2=$IW_p(\nu,R)$}
{wiki}
If $W\sim W_p(\nu,R^{-1})$ has the <Wishart distribution>, then $W^{-1}$ has the inverse-Wishart distribution. For $\nu>p+1$, its <expected value> is $R/(\nu-p-1)$; below this threshold the matrix mean does not exist. It commonly models a <covariance matrix>, but couples the prior behavior of variances and correlations.