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Selecting Models from Data pp 143–152Cite as

Diagnostic systems by model selection: a case study

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 89))

Abstract

Probabilistic systems for diagnosing blue babies are constructed by model selection methods applied to a database of cases. Their performance are compared with a system built primarily by use of expert knowledge. Results indicate that purely automatic methods do not quite perform at the level of expert based systems, but when expert knowledge is incorporated properly, the methods look very promising.

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Author information

Authors and Affiliations

  1. Aalborg University, Denmark

    S. L. Lauritzen & B. Thiesson

  2. MRC, Cambridge, England

    D. J. Spiegelhalter

Authors
  1. S. L. Lauritzen
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  2. B. Thiesson
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  3. D. J. Spiegelhalter
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Editor information

Editors and Affiliations

  1. Ames Research Center, NASA, Mailstop 269-2, 94035, Moffet Field, CA, USA

    P. Cheeseman

  2. Department of Statistics and Actuarial Science, University of Waterloo, N2L 3G1, Waterloo Ontario, Canada

    R. W. Oldford

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© 1994 Springer-Verlag New York, Inc.

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Lauritzen, S.L., Thiesson, B., Spiegelhalter, D.J. (1994). Diagnostic systems by model selection: a case study. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_15

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  • DOI: https://doi.org/10.1007/978-1-4612-2660-4_15

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94281-0

  • Online ISBN: 978-1-4612-2660-4

  • eBook Packages: Springer Book Archive

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