Factor Graph Grammars
David Chiang, Darcey Riley
摘要
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.
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引用它的顶会 Paper5
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- Exact Recursive Probabilistic ProgrammingDavid Chiang, Colin McDonald, Chung-chieh ShanOOPSLA 2023 · 被引用 12 次
- Compiling Probabilistic Programs for Variable Elimination with Information FlowJianlin Li, Eric Wang, Yizhou ZhangPLDI 2024 · 被引用 6 次
- Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian NetworksRobert Lieck, Martin RohrmeierNeurIPS 2021 · 被引用 5 次
- Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive GrammarsSonglin Yang, Roger Levy, Yoon KimACL 2023 · 被引用 1 次
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