pgmpy: Probabilistic Graphical Models using Python
Ankur Ankan
Abinash Panda
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 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.
Graphical Models, Bayesian Networks, Markov Networks, Variable Elimination
DOI10.25080/Majora-7b98e3ed-001