b) Assuming the features are not independent, how many total parameters need to be estimated, accounting for classifying friends, relatives, and co-workers? Now assume we only classify subjects using the first two features, and we replace the discrete feature values with numbers: MessageFrequency | 0 (Annually) 1 (Monthly) 2 (Daily) Age 0 (Child) 1 (Teen) 2 (MiddleAge) 3 (SeniorCitizen) We use a joint Gaussian likelihood for the probability of the two features for each class y: P(X1,x2|y) , and we also will estimate a prior probability for each of the three contact classes. c) Assuming x1 and x2 are not independent, how many parameters need to be learned to compute the posterior probabilities X2) d) Assuming x1 and x2 are independent, how many parameters need to be learned to compute the posterior probabilities X2)