A model is a representation of a real world phenomena. Probability theory gives us a framework to quantify uncertainty in a mathematically rigorous manner [4]. A random variable is a function on the elements in the sample space. A random variable takes on values and the act of a random variable taking on values can be described by a probability distribution. Conceptualizing entities in terms of random variables and representing the joint probability distribution over these entities is essential to statistical modelling and supervised machine learning. A graph is a data structure which consists of a collection of nodes and edges. Graphs as mathematical structures are studied in graph theory [9]. Using graphs to describe probability distributions over random variables gives us a potent way to map the flow of influence, interdependencies and independencies between the random variables. Graph based manipulations give us enhanced computational efficiency and add to the descriptive power, performance of our model.