Abstract
Current methods to learn Bayesian Belief Networks (bbns) from incomplete databases share the common assumption that the unreported data are missing at random. This paper describes a method — called Bound and Collapse (bc) — to learn bbns from incomplete databases which allows the analyst to efficiently integrate information provided by the database and exogenous knowledge about the pattern of missing data. bc starts by bounding the set of estimates consistent with the information conveyed by the database and then collapses the resulting set to a point via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Experiments comparing bc to Gibbs Sampling are provided.
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© 1997 Springer-Verlag
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Ramoni, M., Sebastiani, P. (1997). The use of exogenous knowledge to learn Bayesian Networks from incomplete databases. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052869
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DOI: https://doi.org/10.1007/BFb0052869
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