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The factor graph approach to model-based signal processing
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DOI：10.1109/JPROC.2007.896497.png)
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Abstract
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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 approaches-steepest descent and expectation maximization-to message passing through a multiplier node.
Keywords:
estimation
factor graphs
graphical models
Kalman filtering
message passing
signal processing
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