You would like to use Bayesian classification methods to classify an unknown observation vector z: (px 1) into o , where ~ N(0,2),i=1,2. You have training data with N, observation vectors believed to come from , and N believed to come from 2. Suppose there are errors in the training data so that each training data vector has only a 90 percent chance of having been correctly classified. How would this fact affect your rules for classifying z?