Probabilistic Inference in Artificial Intelligence: The Method of Bayesian Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1993

AUTHORS

Jean-Louis Golmard

ABSTRACT

Bayesian networks are formalisms which associate a graphical representation of causal relationships and an associated probabilistic model. They allow to specify easily a consistent probabilistic model from a set of local conditional probabilities. In order to infer the probabilities of some facts, given observations, inference algorithms have to be used, since the size of the probabilistic models is usually large. Several such inference methods are described and illustrated. Less advanced related problems, namely learning, validation, continuous variables, and time, are briefly discussed. Finally, the relationships between the field of Bayesian networks and other scientific domains are reviewed. More... »

PAGES

257-291

References to SciGraph publications

  • 1991. Inference in possibilistic hypergraphs in UNCERTAINTY IN KNOWLEDGE BASES
  • Book

    TITLE

    Philosophy of Probability

    ISBN

    978-90-481-4301-6
    978-94-015-8208-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-94-015-8208-7_11

    DOI

    http://dx.doi.org/10.1007/978-94-015-8208-7_11

    DIMENSIONS

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