文件名称:Loeliger_factor_graph
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消息传递算法用于信号处理的绝对经典之作，好好学习，天天向上-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.
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.
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Loeliger_factor_graph.pdf