Suppose I am generating a sample for $P(A=0|B=0,C=0,AvB=0,CvA=2,BvC=0)$ The pseudo probability for this would be given as:
$P'(A=0|B=0,C=0,AvB=0,CvA=2,BvC=0) = P(A=0)P(B=0,C=0,AvB=0,CvA=2,BvC=0|A=0)$
$P'(A=0|B=0,C=0,AvB=0,CvA=2,BvC=0) = P(A=0)P(B=0|A=0)P(C=0|A=0)P(AvB=0|A=0)P(CvA=2|A=0)P(BvC=0|A=0)$
$P'(A=0|B=0,C=0,AvB=0,CvA=2,BvC=0) = P(A=0)P(B=0)P(C=0)P(AvB=0|A=0)P(CvA=2|A=0)P(BvC=0)$
We would then compute the pseudo probabilities for the values of A=1,A=2,A=3. add them with the value for A=0 and it becomes our normalizer.
Then we divide the value of pseudo probability of A=0 with normalizer and we get the required answer.
A, B and C are independent of each other. AvB is dependent on A and B and so on.
I am getting the wrong answer for some reason. Could someone point out my mistake? In my opinion the problem lies in the part where I expand the conditional probability in the second step. Thank you