Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
We propose a family of efficient algorithms for learning the parameters of a Bayesian network from incomplete data. Our approach is based on recent theoretical analyses of missing data problems, which utilize a graphical representation, called the missingness graph. In the case of MCAR and MAR data, this graph need not be explicit, and yet we can still obtain closed-form, asymptotically consistent parameter estimates, without the need for inference. When this missingness graph is explicated (based on background knowledge), even partially, we can obtain even more accurate estimates with less data. Empirically, we illustrate how we can learn the parameters of large networks from large datasets, which are beyond the scope of algorithms like EM (which require inference).