This is the public, feature-limited version of the conference webpage. After Registration and login please visit the full version.

Factor Graph Grammars

David Chiang, Darcey Riley

Spotlight presentation: Orals & Spotlights Track 25: Probabilistic Models/Statistics
on 2020-12-10T07:30:00-08:00 - 2020-12-10T07:40:00-08:00
Poster Session 6 (more posters)
on 2020-12-10T09:00:00-08:00 - 2020-12-10T11:00:00-08:00
Abstract: We propose the use of hyperedge replacement graph grammars for factor graphs, or actor 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.

Preview Video and Chat

To see video, interact with the author and ask questions please use registration and login.