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Some Results on the Automatic Construction of Bayesian Networks

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Operations Research ’91

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Abstract

When dealing with systems that contain a great quantity of knowledge, that one of the main problems we need to solve is to determine how much and what sort of knowledge is necessary to perform a given task (e.g. an inference or a diagnosis). In other words, it is essential to know what information is relevant to the question we are interested in.

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References

  1. Acid, S., Campos, L.M. de, González, A., Molina, R. and Pérez de la Blanca, N. (1991) CASTLE: Causal Structures from Inductive Learning. Release 2.0. Technical Report no 91–4–3. DECSAI. Universidad de Granada.

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Author information

Authors and Affiliations

  1. Departamento de Ciencias de la Computatión e I.A., Universidad de Granada, 18071, Granada, Spain

    S. Acid, L. M. de Campos, A. González, R. Molina & N. Pérez de la Blanca

Authors
  1. S. Acid
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  2. L. M. de Campos
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  3. A. González
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  4. R. Molina
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  5. N. Pérez de la Blanca
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Editor information

Editors and Affiliations

  1. FB IV, Mathematik, Universität Trier, Postfach 38 25, D-5500, Trier, Germany

    Peter Gritzmann , Rainer Hettich , Reiner Horst  & Ekkehard Sachs , ,  & 

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© 1992 Physica-Verlag Heidelberg

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Acid, S., de Campos, L.M., González, A., Molina, R., de la Blanca, N.P. (1992). Some Results on the Automatic Construction of Bayesian Networks. In: Gritzmann, P., Hettich, R., Horst, R., Sachs, E. (eds) Operations Research ’91. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-48417-9_150

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  • DOI: https://doi.org/10.1007/978-3-642-48417-9_150

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-0608-3

  • Online ISBN: 978-3-642-48417-9

  • eBook Packages: Springer Book Archive

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