EDML: A Method for Learning Parameters in Bayesian Networks
Abstract
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.
Cite
Text
Choi et al. "EDML: A Method for Learning Parameters in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2011.Markdown
[Choi et al. "EDML: A Method for Learning Parameters in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2011.](https://mlanthology.org/uai/2011/choi2011uai-edml/)BibTeX
@inproceedings{choi2011uai-edml,
title = {{EDML: A Method for Learning Parameters in Bayesian Networks}},
author = {Choi, Arthur and Refaat, Khaled S. and Darwiche, Adnan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2011},
pages = {115-124},
url = {https://mlanthology.org/uai/2011/choi2011uai-edml/}
}