# Probabilities from bayesian network

I am doing problems related to bayesian network. After reading the theory part I am able to understand that by making a network or reducing a problem to some bayesian network, we are simplifying a process for computing the joint probability of events, by minimizing the number of parameters for calculating the joint probabilities. But I have one doubt. Assume that we have obtained a formula for computing the joint probabilities from the network. Can I compute probabilities from this formula for each combination of events or can I do that only after making the table for joint distribution and then finding marginal or conditional probabilities encountered in my problem. I mean that suppose the following is the formula we obtained from the network: P(D,S,G,I,L)=P(D)*P(I)*P(G/(ID))*P(L/G)*P(S/I) And all variables are binary. Now we have tables for each of the probabilities that appear in the above formula. But now I want to know the probability for P(L=0/(S=0,I=1)) from the existing probabilities. What is the process for doing that?

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