Uncertainty in Artificial Intelligence
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"Ideal Parent" Structure Learning for Continuous Variable Networks
Iftach Nachman, Gal Elidan, Nir Friedman
Abstract:
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.
Keywords: null
Pages: 400-409
PS Link:
PDF Link: /papers/04/p400-nachman.pdf
BibTex:
@INPROCEEDINGS{Nachman04,
AUTHOR = "Iftach Nachman and Gal Elidan and Nir Friedman",
TITLE = ""Ideal Parent" Structure Learning for Continuous Variable Networks",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "400--409"
}


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