We have the following Bayesian Belief Network. However, the given network has some missing information marked in grey color. Specifically, we do not know the exact values of variable x and variable y. But, what we know is that y is equal to 2.5x. Besides, we know the following patient. (1) he has his family history (2) he is a smoker (3) his result of X-Ray is negative According to the concept of Bayesian Belief Network (without the missing information), we could compute the probability that he suffers from Lung Cancer is equal to 0.765625 . (a) Is it possible to know the values of variable x and variable y ? If yes, please write down the values of x and y, and give the derivations/explanations. Otherwise, please describe why it is not possible and give the minimal information so that we could derive the values of x and y. (b) Although Bayesian Belief Network classifier does not have an independent assumption among all attributes (compared with the nave Bayesian classifier), what are the disadvantages of this classifier