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Low complexity sparse Bayesian learning using combined belief propagation and mean field with a stretched factor graph
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文摘

A stretched factor graph representation for probabilistic models is introduced.

Advantages of the stretched factor graph over conventional ones are demonstrated.

Novel low cost combined BP-MF message passing algorithms are proposed for SBL.

The new SBL algorithms outperform state-of-the-art message passing SBL algorithms.

This work shows advantages of combined message passing over pure message passing.

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