Uncertainty in Artificial Intelligence
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Efficient Stepwise Selection in Decomposable Models
Amol Deshpande, Minos Garofalakis, Michael Jordan
Abstract:
In this paper, we present an efficient way of performing stepwise selection in the class of decomposable models. The main contribution of the paper is a simple characterization of the edges that canbe added to a decomposable model while keeping the resulting model decomposable and an efficient algorithm for enumerating all such edges for a given model in essentially O(1) time per edge. We also discuss how backward selection can be performed efficiently using our data structures.We also analyze the complexity of the complete stepwise selection procedure, including the complexity of choosing which of the eligible dges to add to (or delete from) the current model, with the aim ofminimizing the Kullback-Leibler distance of the resulting model from the saturated model for the data.
Keywords:
Pages: 128-135
PS Link:
PDF Link: /papers/01/p128-deshpande.pdf
BibTex:
@INPROCEEDINGS{Deshpande01,
AUTHOR = "Amol Deshpande and Minos Garofalakis and Michael Jordan",
TITLE = "Efficient Stepwise Selection in Decomposable Models",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "128--135"
}


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