  We propose an efficient family of algorithms to learn the parameters of a
Bayesian network from incomplete data. In contrast to textbook approaches such
as EM and the gradient method, our approach is non-iterative, yields closed
form parameter estimates, and eliminates the need for inference in a Bayesian
network. Our approach provides consistent parameter estimates for missing data
problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach
is orders of magnitude faster than EM (as our approach requires no inference).
Given sufficient data, we learn parameters that can be orders of magnitude more
accurate.
