Abstract¶
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 computationally 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 is a python library for working with graphical models. It allows the user 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.
