First-order probabilistic inference

11 years 29 days ago
First-order probabilistic inference
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.
David Poole
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Authors David Poole
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