Group 50: Harsh, Willem & Geralda

In this folder the implementation of a Bayesian Network Reasoner can be found for assignement 2 of KR 2022. The following basic functionalities are implemented and can be found in simular named files: Network Pruning - networkpruning.py D-Separation - DSeparation.py Independence - Independece.py Marginalization - Marginalization.py Maxing-out - Factor multiplication - Ordering - Ordering with Min-Degree and Min-Fill Variable Elimination - Marginal Distributions - MAP - MEP -

In the test_implementation.py we will walk you through tests per functionality to test if the code works.