论文标题
因子图语法
Factor Graph Grammars
论文作者
论文摘要
我们建议将HyperDege替换图语法用于因子图或因子图语法（FGGS）简称。 FGGS生成了一组因子图，并且可以描述比板符号，动态图形模型，案例因子图和总和产品网络可以描述更通用的模型类别。此外，可以在FGG上进行推断，而无需枚举所有生成的因子图。对于有限的可变域（但可能是无限的图表集），在许多情况下，将可变消除的概括允许精确且可拖动的推断。对于有限的图形集（但可能是无限的可变域），可以将FGG转换为可将标准推理技术的单个因子图转换为单个因素图。
We propose the use of hyperedge replacement graph grammars for factor graphs, or factor graph grammars (FGGs) for short. FGGs generate sets of factor graphs and can describe a more general class of models than plate notation, dynamic graphical models, case-factor diagrams, and sum-product networks can. Moreover, inference can be done on FGGs without enumerating all the generated factor graphs. For finite variable domains (but possibly infinite sets of graphs), a generalization of variable elimination to FGGs allows exact and tractable inference in many situations. For finite sets of graphs (but possibly infinite variable domains), a FGG can be converted to a single factor graph amenable to standard inference techniques.
