Translating Recursive Probabilistic Programs to Factor Graph Grammars

• URL: http://arxiv.org/abs/2010.12071v1
• Date: Thu, 22 Oct 2020 21:17:04 GMT
• Title: Translating Recursive Probabilistic Programs to Factor Graph Grammars
• Authors: David Chiang and Chung-chieh Shan
• Abstract summary: A factor graph grammar (FGG) generates a set of factor graphs that do not all need to be enumerated in order to perform inference. We provide a semantics-preserving translation from first-order probabilistic programs with conditionals and recursion to FGGs.
• Score: 20.539191533339427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is natural for probabilistic programs to use conditionals to express alternative substructures in models, and loops (recursion) to express repeated substructures in models. Thus, probabilistic programs with conditionals and recursion motivate ongoing interest in efficient and general inference. A factor graph grammar (FGG) generates a set of factor graphs that do not all need to be enumerated in order to perform inference. We provide a semantics-preserving translation from first-order probabilistic programs with conditionals and recursion to FGGs.

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