We propose a method called EDML for learning MAP parameters in binary Bayesian net- works 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 com- pared to EM. We introduce EDML, explain its origin, and study some of its properties both analytically and empirically.
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