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Parameter Learning in Object-Oriented Bayesian Networks

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Abstract

This paper describes a method for parameter learning in Object-Oriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in object-oriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are not strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to object-oriented domains.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491, Trondheim, Norway

    Helge Langseth

  2. Department of Computer Science, Aalborg University, Fredrik Bajers Vej 7E, DK-9220, Aalborg Øst, Denmark

    Helge Langseth & Olav Bangsø

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  1. Helge Langseth
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  2. Olav Bangsø
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Langseth, H., Bangsø, O. Parameter Learning in Object-Oriented Bayesian Networks. Annals of Mathematics and Artificial Intelligence 32, 221–243 (2001). https://doi.org/10.1023/A:1016769618900

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