Bayesian Inference
FAIKR MOD3 project
Implementation of Prior, Rejection, Likelihood and Gibbs Sampling
Implementation of exact inference on Bayesian Network using Junction Trees in Python
This project implements both exact and approximate inference techniques for Bayesian Networks using enumeration and rejection sampling, respectively. It processes Bayesian Network structures in XMLBIF format, accepting command-line inputs to compute the posterior distribution of a query variable given observed evidence.
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