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Inconsistent Knowledge Integration in a Probabilistic Model

In: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

Author

Listed:
  • Radim Jiroušek

    (Institute of Information Theory and Automation, Department of Decision-Making Theory
    University of Economics, Department of Information and Knowledge Engineering Faculty of Informatics and Statistics)

  • Jiří Vomlel

    (Institute of Information Theory and Automation, Department of Decision-Making Theory)

Abstract

In probabilistic models of knowledge based systems, the knowledge base is usually represented by a multidimensional probability distribution (Hájek et. al., 1992). Analogously, oligodimensional distributions (i.e. distributions of dimensionality cca 1 – 5) can be considered partial knowledge. Within this framework the knowledge integration process corresponds to constructing a multidimensional distribution with the given marginals. For this purpose the well known Iterative Proportional Fitting (IPF) procedure can be used.

Suggested Citation

  • Radim Jiroušek & Jiří Vomlel, 1995. "Inconsistent Knowledge Integration in a Probabilistic Model," Springer Books, in: Giulianella Coletti & Didier Dubois & Romano Scozzafava (ed.), Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, pages 263-270, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4899-1424-8_18
    DOI: 10.1007/978-1-4899-1424-8_18
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