Discrimination of Semi-Quantitative Models by Experiment Selection: Method Application in Population Biology

Ivayla Vatcheva, Olivier Bernard, Hidde de Jong, Jean-Luc Gouze, Nicolaas Mars

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    Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semiquantitative differential equations. The method is a generalization of previous work in model discrimination. It is based on an entropy criterion for the selection of the most informative experiment which can handle cases where the models predict multiple qualitative behaviors. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor.
    Original languageUndefined
    Title of host publicationProceedings 2001 International Joint Conference on Artificial Intelligence
    EditorsB. Nebel
    Place of PublicationSan Francisco, USA
    PublisherMorgan Kaufmann
    Number of pages6
    ISBN (Print)1-55860-777
    Publication statusPublished - 5 Aug 2001
    EventInternational Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA
    Duration: 4 Aug 200110 Aug 2001


    ConferenceInternational Joint Conference on Artificial Intelligence, IJCAI 2001
    Other4-10 August 2001


    • IR-59861
    • METIS-203435

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