How is HUGIN treating missing data in parameter learning? When I include missing data in the data input files and use the EM algorithm to learn the CPT tables, I get non-integer experience values. When I remove the rows with missing data, I get only integer experience values.) How is HUGIN extrapolating to calculate these non-integer experience values?
At the start of an iteration, we have CPTs for all nodes in the network (the CPTs are updated at the end of each iteration, so the CPTs for the next iteration will be better). An incomplete case is entered as evidence in the network, and the evidence is propagated.
If not all parents of a node in the case are instantiated, we get a probability distribution over the parents. Each probability in this distribution is the contribution of the case to the experience count of the corresponding parent configuration. This is where the non-integer counts arise (if all cases are complete, the probabilities will all be 0 or 1).
