Uncertainty in Artificial Intelligence
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A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Jin Tian
Abstract:
This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch-and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL) principle, for a given (consistent) variable ordering. The algorithm exhaustively searches through all network structures and guarantees to find the network with the best MDL score. Preliminary experiments show that the algorithm is efficient, and that the time complexity grows slowly with the sample size. The algorithm is useful for empirically studying both the performance of suboptimal heuristic search algorithms and the adequacy of the MDL principle in learning Bayesian networks.
Keywords: structure learning, learning Bayesian networks
Pages: 580-588
PS Link: http://www.cs.ucla.edu/~jtian/UAI00.ps
PDF Link: /papers/00/p580-tian.pdf
BibTex:
@INPROCEEDINGS{Tian00,
AUTHOR = "Jin Tian ",
TITLE = "A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "580--588"
}


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