= Inverse-Wishart distribution
{title2=$IW_p(\nu,R)$}
{wiki}
For <positive-definite matrix>[positive-definite] $R$ and $\nu>p-1$, 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 distribution>[prior] behavior of <variance>[variances] and correlations.
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