Abstract
Graphical models based on conditional independence provide a concise representation of the subjective belief about the variables relationships of a single expert. Faced to the task of constructing a large model, we may find that each expert might be specialist in some subset of the complete domain. It may be desirable to aggregate the knowledge provided by those specialists, under the form of related graphical models, into a single more general representation. This paper introduces a new model for combining the graphs associated to two Bayesian networks into a single one, which may be used as consensus model.
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© 2001 Springer-Verlag Wien
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del Sagrado, J., Moral, S. (2001). Qualitative Aggregation of Bayesian Networks. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Data Fusion and Perception. International Centre for Mechanical Sciences, vol 431. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2580-9_5
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DOI: https://doi.org/10.1007/978-3-7091-2580-9_5
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83683-5
Online ISBN: 978-3-7091-2580-9
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