A BN is a probabilistic graphical model which is used to represent knowledge about an uncertain domain. Each random variable (a variable whose possible values are outcomes of a random phenomenon) is presented by a node in the BN and to each node, there is attached a conditional probability table. In general, three classes of nodes exist in BN: (i) nodes without a child node are called leaf nodes, (ii) nodes without a parent node are called root nodes, and (iii) nodes with parent and child nodes are called intermediate nodes. The directed links (edges) between the nodes represent probabilistic dependencies among these nodes. The direct casual relation between two nodes shows that the corresponding nodes will have a greater influence on the system than others. The only constraint on the links allowed in a BN is that there must not be any directed cycles: one cannot return to a node simply by following a series of directed links.