Expectation propagation is a general approach to fast approximate inference for graphical models. The existing literature treats models separately when it comes to deriving and coding expectation propagation inference algorithms. This comes at the cost of similar, long-winded algebraic steps being repeated and slowing down algorithmic development. We demonstrate how factor graph fragmentization can overcome this impediment. This involves adoption of the message passing on a factor graph approach to expectation propagation and identification of factor graph sub-graphs, which we call fragments, that are common to wide classes of models. Key fragments and their corresponding messages are catalogued which means that their algebra does not need to be repeated. This allows compartmentalization of coding and efficient software development.

中文翻译：

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期望传播的因子图碎片化

期望传播是一种用于图形模型的快速近似推断的通用方法。当涉及到预期传播推理算法的推导和编码时，现有文献将模型分开对待。这是以重复类似的，冗长的代数步骤为代价的，并且减慢了算法的开发速度。我们演示了因子图碎片化如何克服这一障碍。这涉及采用通过因子图方法传递的消息来进行期望传播和因子图子图的识别，我们称其为片段，这是各种模型所共有的。密钥片段及其对应的消息被分类，这意味着它们的代数不需要重复。这样可以分隔编码并进行有效的软件开发。