Uncertainty in Artificial Intelligence
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Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
James Myers, Kathryn Laskey, Tod Levitt
Abstract:
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.
Keywords: Bayesian Networks, learning, stochastic algorithms, MCMC, evolutionary algorithms
Pages: 476-485
PS Link: http://ite.gmu.edu/~klaskey/lectures.html
PDF Link: /papers/99/p476-myers.pdf
BibTex:
@INPROCEEDINGS{Myers99,
AUTHOR = "James Myers and Kathryn Laskey and Tod Levitt",
TITLE = "Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "476--485"
}


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