variance parameter of your Gaussian will actually be the empirical variance on your samples not too surprising but formally derived that that is actually the right thing to do to do maximum likelihood estimation for a Gaussian how about conditional Gaussian but this litigation would be where you have a distribution where Y is effectively a linear regression of X but it could be higher dimensional of course y equals a0 plus a1x plus noise that's a linear Gaussian from one D to one D you can get a bunch of samples work through it and