Parameter Estimation in Bayesian Networks from Incomplete Databases
Current methods to learn Bayesian Networks 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 Bayesian Networks from incomplete databases which allows the analyst to efficiently integrate the information provided by the database and the exogenous knowledge about the pattern of missing data. BC starts by bounding he 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 BC to the Gibbs Samplings are also provided.
1. Knowledge Media Institute, The Open University.
2. Department of Actuarial Science and Statistics, City University.