English

Factor Graph Grammars

Machine Learning 2020-10-26 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2010.12048,
  title  = {Factor Graph Grammars},
  author = {David Chiang and Darcey Riley},
  journal= {arXiv preprint arXiv:2010.12048},
  year   = {2020}
}

Comments

Accepted for presentation at NeurIPS 2020