2012/07/04 by Peter Carbonetto, Jacek Kisyński, Carbonetto, Peter +5 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1207.1375
openalex publication_date 2012/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By exploiting exchangeability, distributions over unknown objects and their attributes are cast as Dirichlet processes, which resolve difficulties in model selection and inference caused by varying numbers of objects. We demonstrate these concepts with application to citation matching.