Lifted first-order probabilistic inference

Lifted first-order probabilistic inference (2005)

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by Rodrigo De Salvo Braz , Eyal Amir , Dan Roth

Venue:	In Proceedings of IJCAI-05, 19th International Joint Conference on Artificial Intelligence
Citations:	56 - 6 self

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@INPROCEEDINGS{Braz05liftedfirst-order,
    author = {Rodrigo De Salvo Braz and Eyal Amir and Dan Roth},
    title = {Lifted first-order probabilistic inference},
    booktitle = {In Proceedings of IJCAI-05, 19th International Joint Conference on Artificial Intelligence},
    year = {2005},
    pages = {1319--1325},
    publisher = {Morgan Kaufmann}
}

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Abstract

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 paper we present the first exact inference algorithm that 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. 1

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