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Expectation propagation is a general approach to fast approximate inferencefor graphical models. The existing literature treats models separately when itcomes to deriving and coding expectation propagation inference algorithms. Thiscomes at the cost of similar, long-winded algebraic steps being repeated andslowing down algorithmic development. We demonstrate how factor graphfragmentization can overcome this impediment. This involves adoption of themessage passing on a factor graph approach to expectation propagation andidentification of factor graph sub-graphs, which we call fragments, that arecommon to wide classes of models. Key fragments and their correspondingmessages are catalogued which means that their algebra does not need to berepeated. This allows compartmentalization of coding and efficient softwaredevelopment.

Factor graph fragmentization of expectation propagation