Probabilistic Graphical Models (PGM) is a technique of compactly representing a joint distribution by exploiting dependencies between the random variables. It also allows us to do inference on joint distributions in a computation-ally cheaper way than the traditional methods. PGMs are widely used in the field of speech recognition, information extraction, image segmentation, modelling gene regulatory networks. pgmpy [pgmpy] is a python library for working with graphical models. It allows the

more »

... r to create their own graphical models and answer inference or map queries over them. pgmpy has implementation of many inference algorithms like VariableElimination, Belief Propagation etc. This paper first gives a short introduction to PGMs and various other python packages available for working with PGMs. Then we discuss about creating and doing inference over Bayesian Networks and Markov Networks using pgmpy.