A Bayesian method for the induction of probabilistic networks from data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1992-10

AUTHORS

Gregory F. Cooper, Edward Herskovits

ABSTRACT

This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems. More... »

PAGES

309-347

References to SciGraph publications

  • 1977. Counting unlabeled acyclic digraphs in COMBINATORIAL MATHEMATICS V
  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • 1986. Machine Learning of Inductive Bias in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00994110

    DOI

    http://dx.doi.org/10.1007/bf00994110

    DIMENSIONS

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