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.