Simulink Library for the learning and inference using Factor Graph in Reduced Normal Form paradigm.
Remember to add directory LibFactorGraph to the MATLAB path.
A tutorial is present in the Tutorial/Example1.m
The data observed (training set) are injected in the network (designed using Simulink and the objects present in LibFactorGraph/LibFactorGraph.mdl and the conditional probability tables (CPTs) are learned and printed on screen at the end of Learning process.
Using the learned CPTs and particular values for three variables (A, S, C) injected in the network as forward messages for A and S (fA, fS) and as backward message for C (bC), we obtain, after the belief propagation, the inference (bA, bS, fC).
If you use this library, please cite the paper https://link.springer.com/chapter/10.1007/978-3-319-18164-6_2
Buonanno, A., Palmieri, F.A.N. (2015). Simulink Implementation of Belief Propagation in Normal Factor Graphs. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_2