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Efficient Parameter Learning in Bayesian Networks from Incomplete Databases

Current methods to learn conditional probabilities from incomplete databases use a common strategy: they complete the database by inferring somehow the missing data from the available information and then learn from the completed database. This paper introduces a new method - called bound and collapse (BC) - which does not follow this strategy. BC starts by bounding the set of estimates consistent with the available information and then collapses the resulting set to a point estimate via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Experiments comparing c to the Gibbs Samplings are also provided.

1. Knowledge Media Institute, The Open University.

2. Department of Actuarial Science and Statistics, City University.

ID: kmi-97-03

Date: 1997

Author(s): Marco Ramoni and Paola Sebastiani

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