We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief propagation and other heuristics fail to converge. Belief propagation is guaranteed to converge when the underlying graphical model is acyclic, but can fail to converge and is sensitive to initialization when the underlying graph has complex topology. This paper describes modifications to the standard belief propagation algorithms that lead to methods that converge to unique solutions on graphical models with arbitrary topology and potential functions.

翻译：我们提出了用于用图形模型进行推论的新的信息传递算法。 我们的方法针对的是循环信仰传播和其他超自然学无法汇合的最困难的推论问题。 当基本图形模型是环状的时,信仰传播可以保证会汇合,但不能汇合,并且当基本图形具有复杂的地形学时,对初始化十分敏感。本文描述了对标准信仰传播算法的修改,这些修改导致在带有任意地形学和潜在功能的图形模型上找到独特解决方案的方法。