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Lifted First-Order Probabilistic Inference Rodrigo de Salvo Braz and Eyal Amir and Dan Roth
 

Summary: Lifted First-Order Probabilistic Inference
Rodrigo de Salvo Braz and Eyal Amir and Dan Roth
University of Illinois at Urbana-Champaign
Department of Computer Science
201 N Goodwin Ave, Urbana, IL 61801-2302
braz@uiuc.edu, {eyal,danr}@cs.uiuc.edu
Abstract
Most probabilistic inference algorithms are speci-
fied and processed on a propositional level. In the
last decade, many proposals for algorithms accept-
ing 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 di-
rectly 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 gen-
eralizes undirected graphical models). Our exper-

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences