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Structure learning approaches in causal probabilistics networks

  • P. Larrañaga
  • Y. Yurramendi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 747)

Abstract

Causal Probabilistic Networks (CPN), a method of reasoning using probabilities, has become popular over the last few years within the AI probability and uncertainty community. This paper begins with an introduction to this paradigm, followed by a presentation of some of the current approaches in the induction of the structure learning in CPN. The paper concludes with a concise presentation of alternative approaches to the problem, and the conclusions of this review.

Keywords

Genetic Algorithm Simulated Annealing Markov Random Field Structure Learning Markov Network 
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 1993

Authors and Affiliations

  • P. Larrañaga
    • 1
  • Y. Yurramendi
    • 1
  1. 1.Dept. of Computer Science and Artificial IntelligenceUniversity of the Basque CountryDonostiaSpain

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