Hello this is most definitely a question for dummies i feel. The question concern bayesian network and inference thereof. I've heard about the product rule, bayes theorem and the chain rule. However this bayesian network still keeps eluding me... So i hope someone could give a quick recap on how i should think the following assignment which i have created based on some research.
So giving the following problem statement:
The Bayesian Network LMV below has three nodes for boolean variables, L, M and V. The probabilities for L and M are: P (M = true) = 0.2 and P (L = true) = 0.7. The conditional probabilities for variable V are as shown in the table below:
Question: What value is the value of P(V = false | L = false) ?
How is this computed? This realy confuses me because i dont get how we can isolate P(V = False | L= False) when the image suggest that we only have the conditional probability of V given both L and M? Any help appreciated :)