Sample T = 1000 observations from model (1.1) with d = 1 using your preferred choice of values for

(i) and vi for i = 12 with (i) < 1 and (1)= (2) Assuming that d is known and using prior distributions of the form p( (i)) = N(0c), for i = 12 and some c > 0, p( ) =

U(

aa) and p(v) = IG( 0 0), with a > 0 0 > 0 and 0 > 0 obtain samples from the joint posterior distribution by implementing a Metropolis-Hastings algorithm.