Translating Recursive Probabilistic Programs to Factor Graph Grammars
This work addresses the problem of scalable inference for researchers and practitioners using recursive probabilistic programs, though it appears incremental as it builds on existing translation methods to factor graph grammars.
The authors tackled the challenge of performing efficient inference in probabilistic programs that use conditionals and recursion by developing a semantics-preserving translation from such programs to factor graph grammars, which avoids enumerating all possible factor graphs.
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.