Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Codes on graphs: normal realizations
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
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Inference on factor graphs with loops with the standard forward-backward algorithm, can give unpredictible results as messages can travel indefinitely in the system with no guarantee on convergence. We apply the exact method of cutset conditioning to Factor Graphs with loops starting from a fully developed three-variable example and providing comments and suggestions for distributed implementations.