The Bayesian setup: The posterior distribution 1.0/2 points (graded) Observe that if , and are given, then each is a gaussian: . Therefore, the likelihood function of the vector given is of the form It turns out that the distribution of given and is a 2-dimensional Gaussian. In terms of , and , what is its mean and covariance matrix? Hint: look ahead and see what part (b) is asking. What answer do you hope would come out, at least for one of these two things? (Type X for , trans(X) for the transpose , and X^(-1) for the inverse of a matrix .)