Lifted first-order probabilistic inference

Rodrigode Salvo Braz, Eyal Amir, Dan Roth

Research output: Contribution to journalConference articlepeer-review


Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the first-order level, but this method is limited to special cases. In this paperwe present the first exact inference algorithmthat operates directly on a first-order level, and that can be applied to any first-order model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference.

Original languageEnglish (US)
Pages (from-to)1319-1325
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: Jul 30 2005Aug 5 2005

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Lifted first-order probabilistic inference'. Together they form a unique fingerprint.

Cite this