New Advances in Logic-Based Probabilistic Modeling by PRISM

  • Taisuke Sato
  • Yoshitaka Kameya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4911)


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.


Bayesian Network Logic Program Logic Programming Probability Computation 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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© 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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