Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once per group, as opposed to once per variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing approaches for exact lifted inference use specific languages for (in)equality constraints, which often have limited expressivity. In this article, we decouple lifted inference from the constraint language. We define operators for lifted inference in terms of relational algebra operators, so that they operate on the semantic level (the constraints extension) rather than on the syntactic level, making them language-independent. As a result, lifted inference can be performed using more powerful constraint languages, which provide more opportunities for lifting. We empirically demonstrate that this can improve inference efficiency by orders of magnitude, allowing exact inference where until now only approximate inference was feasible.

该文章将提升的推断与约束语言解耦，并通过关系代数运算符定义提升的推断算子，使它们能够在语义层面（约束扩展）上进行操作，从而使其与语言无关。通过使用更强有力的约束语言，飞跃推断的效率得到了数量级的提升，从而允许进行精确推断，而不是仅使用近似推断。

提升变量消除：将运算符与约束语言解耦