提升变量消除：从约束语言中解耦运算符
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
摘要：
提升概率推理算法利用图形模型结构中的随机性来更有效地执行推理。更具体地说，它们识别可互换的变量组，并对每组执行一次推理，而不是对每个变量执行一次。分组是通过约束来定义的，因此分组的灵活性取决于约束语言的表达能力。现有的精确提升推理方法使用特定的语言（在）等式约束，这通常具有有限的表达能力。在本文中，我们将提升推理与约束语言解耦。我们定义的操作符提升推理的关系代数运算符，使他们的操作语义层面上（的约束扩展），而不是在语法层面上，使他们的语言独立。因此，可以使用更强大的约束语言来执行提升推理，这为提升提供了更多的机会。我们的经验表明，这可以提高推理效率的数量级，允许精确的推理，直到现在只有近似推理是可行的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
