Compiling Relational Bayesian Networks for Exact Inference

Manfred Jaeger, Adnan Darwiche, Mark Chavira

Research output: Contribution to journalJournal articleResearchpeer-review

63 Citations (Scopus)

Abstract

We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters with hundreds of variables.
Original languageEnglish
JournalInternational Journal of Approximate Reasoning
Volume42
Issue number1-2
Pages (from-to)4-20
ISSN0888-613X
Publication statusPublished - 2006

    Fingerprint

Cite this