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
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Codes on graphs: normal realizations
IEEE Transactions on Information Theory
Notes on Cutset Conditioning on Factor Graphs with Cycles
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
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Many applications that involve inference and learning in signal processing, communication and artificial intelligence can be cast into a graph framework. Factor graphs are a type of network that can be studied and solved by propagating belief messages with the sum/product algorithm. In this paper we provide explicit matrix formulas for inference and learning in finite alphabet Forney-style factor graphs, with the precise intent of allowing rapid prototyping of arbitrary topologies in standard software like MATLAB.