• DocumentCode
    1049193
  • Title

    Explanation of Bayesian Networks and Influence Diagrams in Elvira

  • Author

    Lacave, Carmen ; Luque, Manuel ; Díez, Francisco Javier

  • Author_Institution
    Univ. of Castilla-La Mancha, Ciudad Real
  • Volume
    37
  • Issue
    4
  • fYear
    2007
  • Firstpage
    952
  • Lastpage
    965
  • Abstract
    Bayesian networks (BNs) and influence diagrams (IDs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, alleviating users´ reluctance to accept their advice, and using them as tutoring systems. This paper describes some explanation options for BNs and IDs that have been implemented in Elvira and how they have been used for building medical models and teaching probabilistic reasoning to pre- and postgraduate students.
  • Keywords
    Bayes methods; computer graphics; diagnostic expert systems; inference mechanisms; intelligent tutoring systems; mathematics computing; medical diagnostic computing; Bayesian networks; Elvira; decision-support expert system; diagnosis-expert system; influence diagrams; medical models; postgraduate student; pregraduate student; probabilistic graphical models; probabilistic reasoning teaching; reasoning explanation; tutoring system; Artificial intelligence; Bayesian methods; Debugging; Diagnostic expert systems; Expert systems; Graphical models; Humans; Intelligent systems; Intrusion detection; Medical expert systems; Bayesian networks (BNs); Elvira; expert systems; explanation; influence diagrams (IDs); Algorithms; Artificial Intelligence; Bayes Theorem; Computer Simulation; Decision Support Techniques; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated; Software;
  • fLanguage
    English
  • Journal_Title
    Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4419
  • Type

    jour

  • DOI
    10.1109/TSMCB.2007.896018
  • Filename
    4267869