5. Given the Bayesian network shown in Figure 0.1 (Assume that all the attributes are binary, i.e., the value can be 0 or 1). P(B=0| A=1, C=1)=0.1 P(B=0| A=1, C=0)=0.8 P(B=0| A=0, C=1)=0.2 P(B=0| A=0, C=0)=0.9 P(A=0)=0.1 C P(C=0)=0.2 P(D=0| A=1, C=1)=0.1 P(D=0 | A=1, C=0)=0.8 P(D=0 | A=0, C=1)=0.9 P(D=0 | A=0, C=0)=1.0 Figure 0.1: Bayesian Belief Network (a) Is there any variable conditionally independent of another one given its parent(s)? If so, list all and state your reasons. [4] (b) Compute P(A=1, B=0, C=0, D=1). Show your work. Hint: textbook, page 395, formula 9.1 or lecture notes(Classification Part 1), page 57. [5] (c) Compute P(A=0, B=1, C=0, D=0). Show your work. Hint: textbook, page 395, formula 9.1 or lecture notes(Classification Part 1), page 57. [5] (d) Compute P(D=1| A=0). Show your work. [6]