# 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:

$\hspace{10ex}P(D,S,G,I,L)=P(D)\cdot P(I)\cdot P(G\mid(ID))\cdot P(L\mid G)\cdot P(S\mid I)$

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 $P(L=0\mid (S=0,I=1))$ from the existing probabilities. What is the process for doing that?