论文标题
探索概率关系模型中未知的宇宙
Exploring Unknown Universes in Probabilistic Relational Models
论文作者
论文摘要
大型概率模型通常是由已知个体（宇宙）及其之间的关系池塑造的。提起的推理算法处理已知个体的可进行推理的集合。但是，宇宙可能并不总是被知道，或者只能通过“小宇宙更有可能”（例如“小宇宙”）描述。没有宇宙，不再可能推理提起算法，从而失去了可拖动推理的优势。本文的目的是为具有未知宇宙的模型定义一种语义，从特定的约束语言解耦，从而可以提升，从而可以推断。
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as "small universes are more likely". Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.
