6.22 ( ) Consider a regression problem with N training set input vectors x1, . . . , xN and L test set input vectors xN+1, . . . , xN+L, and suppose we define a Gaussian process prior over functions t(x). Derive an expression for the joint predictive distribution for t(xN+1), . . . , t(xN+L), given the values of t(x1), . . . , t(xN). Show the marginal of this distribution for one of the test observations tj where N + 1
j

N + L is given by the usual Gaussian process regression result (6.66) and (6.67).