pgmpy: Implementation of sampling algorithms for approx. inference in PGMs

by Pratyaksh for Python Software Foundation

Currently pgmpy supports algorithms like Variable Elimination and Belief Propagation for exact inference. In large graphs, exact inference becomes computationally intractable. Thus, there's a need for approximate algorithms which answer the inference query with a lower time complexity. In this project I will implement the two most popular sampling algorithms for inference which fall under the general class of Markov Chain Monte Carlo methods: Gibbs Sampling and Metropolis–Hastings algorithm etc.