Semantics and Inference for Recursive Probability Models
In recent years, there have been several proposals that extend the expressive power of Bayesian networks with that of rela- tional models. These languages open the possibility for the specification of recursive probability models, where a vari- able might depend on a potentially infinite (but finitely de- scribable) set of variables. These models are very natural in a variety of applications, e.g., in temporal, genetic, or language models. In this paper, we provide a structured representa- tion language that allows us to specify such models, a clean measure-theoretic semantics for this language, and a proba- bilistic inference algorithm that exploits the structure of the language for efficient query-answering.