Uncertainty in Artificial Intelligence
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The Factored Frontier Algorithm for Approximate Inference in DBNs
Kevin Murphy, Yair Weiss
Abstract:
The Factored Frontier (FF) algorithm is a simple approximate inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead of doing an exact update at every step followed bymarginalisation (projection), it always works with factoreddistributions. Hence it can be applied to models for which the exactupdate step is intractable. We show that FF is equivalent to (oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the nodes. We then show empirically that byiterating, LBP can improve on the accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs: the first is a modelof a water treatment plant, and the second is a coupled HMM, used tomodel freeway traffic.
Keywords:
Pages: 378-385
PS Link:
PDF Link: /papers/01/p378-murphy.pdf
BibTex:
@INPROCEEDINGS{Murphy01,
AUTHOR = "Kevin Murphy and Yair Weiss",
TITLE = "The Factored Frontier Algorithm for Approximate Inference in DBNs",
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 = "378--385"
}


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