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

| 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 message 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 techniques, which is exemplified by two different approachesV steepest descent and expectation maximizationVto message passing through a multiplier node.