Given a diagram of a Bayesian Network, we can use it to determine the marginal and conditional independence of events in the network (assuming of course, that it is a complete network). Let us assume that we are trying to determine the marginal independence of events A and B and their conditional independence w.r.t event C. Algorithm : 1. Retain only that part of the graph that contains A, B, and nodes that point to them. 2. Connect all pairs of unconnected nodes, where both nodes point to a common node. Make the graph undirected. 3. If there is no path between A and B, they are marginally independent. If there is no path between A and B after removing C, then A and B are conditionally independent given C.