Learning Bayesian Networks from Incomplete Data: An Efficient Method for Generating Approximate Predictive Distributions

We present an efficient method for learning Bayesian network models and parameters from incomplete data. With our approach an approximation is obtained of the predictive distribution. By way of this distribution any learning algorithm that works for complete data can be easily adapted to work for incomplete data as well. Our method exploits the dependence relations between the variables explicitly given by the Bayesian network model to predict missing values. Based on strength of influence and predictive quality, a subset of those predictor variables is selected, from which an approximate predictive distribution is generated by taking the observed part of the data into consideration. The approximate predictive distribution is obtained by traversing the data sample only twice and no iteration is required. Therefore our algorithm is more efficient than iterative algorithms such as EM and SEM. Our experiments show that the method performs well both for parameter learning and model learning compared to EM and SEM.