EDML: A Method for Learning Parameters in Bayesian Networks
  We propose a method called EDML for learning MAP parameters in binary
Bayesian networks under incomplete data. The method assumes Beta priors and can
be used to learn maximum likelihood parameters when the priors are
uninformative. EDML exhibits interesting behaviors, especially when compared to
EM. We introduce EDML, explain its origin, and study some of its properties
both analytically and empirically.
