论文标题
将递归概率程序转换为图形语法
Translating Recursive Probabilistic Programs to Factor Graph Grammars
论文作者
论文摘要
概率程序使用条件来表达模型中的替代子结构和循环（递归）以在模型中表达重复的子结构是很自然的。因此，具有条件和递归的概率计划激发了人们对高效和一般推论的持续兴趣。一个因子图语法（FGG）生成了一组因子图，这些因素图并非全部需要枚举才能执行推断。我们提供了从一阶概率程序，有条件和递归到FGG的语义传播翻译。
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.
