Guide to pgmpy: Probabilistic Graphical Models with Python Code

Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use of independent conditions between the random variables to create a graph structure representing the relationships between different random variables. Further, we can calculate the joint probability distribution of these variables by combining various parameters taken from the graph. What are the types of Graph Models? Mainly, there are two types of Graph models:Bayesian Graph Models:  These models consist of Directed-Cyclic Graph(DAG) and there is always a conditional probability associated with the random variables. These types of models represent causation between the r