Probabilistic Inductive Logic Programming pp 118-155

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4911)

New Advances in Logic-Based Probabilistic Modeling by PRISM

  • Taisuke Sato
  • Yoshitaka Kameya

Abstract

We review a logic-based modeling language PRISM and report recent developments including belief propagation by the generalized inside-outside algorithm and generative modeling with constraints. The former implies PRISM subsumes belief propagation at the algorithmic level. We also compare the performance of PRISM with state-of-the-art systems in statistical natural language processing and probabilistic inference in Bayesian networks respectively, and show that PRISM is reasonably competitive.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Taisuke Sato
    • 1
  • Yoshitaka Kameya
    • 1
  1. 1.Tokyo Institute of Technology, Ookayama Meguro TokyoJapan

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