  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.
