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Evaluating Inference Algorithms for the Prolog Factor Language

  • Tiago Gomes
  • Vítor Santos Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7842)

Abstract

Over the last years there has been some interest in models that combine first-order logic and probabilistic graphical models to describe large scale domains, and in efficient ways to perform inference on these domains. Prolog Factor Language (PFL) is a extension of the Prolog language that allows a natural representation of these first-order probabilistic models (either directed or undirected). PFL is also capable of solving probabilistic queries on these models through the implementation of four inference algorithms: variable elimination, belief propagation, lifted variable elimination and lifted belief propagation. We show how these models can be easily represented using PFL and then we perform a comparative study between the different inference algorithms in four artificial problems.

Keywords

Logic Program Belief Propagation Inference Algorithm Factor Graph Probabilistic Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tiago Gomes
    • 1
  • Vítor Santos Costa
    • 1
  1. 1.CRACS & INESC TEC, Faculty of SciencesUniversity of PortoPortoPortugal

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