Uncertainty in Artificial Intelligence
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EDML: A Method for Learning Parameters in Bayesian Networks
Arthur Choi, Khaled Refaat, Adnan Darwiche
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.
Keywords:
Pages: 115-124
PS Link:
PDF Link: /papers/11/p115-choi.pdf
BibTex:
@INPROCEEDINGS{Choi11,
AUTHOR = "Arthur Choi and Khaled Refaat and Adnan Darwiche",
TITLE = "EDML: A Method for Learning Parameters in Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "115--124"
}


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