Is it possible to learn a distribution over the parameters ($K=\Sigma^{-1}$ and $\mu$) of a Gaussian from noisy measurements of $X$? (Starting with some appropriate prior over the parameters)

I know the case where the measurements are perfect is established theory and can be found in many text books, but I cannot find a solution for this variation of the problem anywhere.