The Factor Graph Approach to Model-Based Signal Processing Factor graphs can model complex systems and help to design effective algorithms for detection and estimation problems.
Hans‐Andrea Loeliger, Justin Dauwels, Junli Hu, S. Korl, Li Ping, Frank R. Kschischang · Proceedings of the IEEE · 2007
The message-passing approach to model-based signal processing is developed with a focus on Gaussian message passing in linear state-space models, which includes recursive least squares, linear minimum-mean-squared-error estimation, and Kalman filtering algorithms. Tabulated mes- sage computation rules for the building blocks of linear models allow us to compose a variety of such algorithms without additional derivations or computations. Beyond the Gaussian case, it is emphasized that the message-passing approach encourages us to mix and match different algorithmic tech- niques, which is exemplified by two different approachesV steepest descent and expectation maximizationVto message passing through a multiplier node.