Uncertainty in Artificial Intelligence
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Learning Equivalence Classes of Bayesian Networks Structures
David Chickering
Abstract:
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring function, it is appropriate for the heuristic search algorithm to search over equivalence classes of Bayesian networks as opposed to individual structures. We present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures. Using this space, any one of a number of heuristic search algorithms can easily be applied. We compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual Bayesian network structures.
Keywords: Learning Bayesian networks, heuristic search, equivalence classes
Pages: 150-157
PS Link:
PDF Link: /papers/96/p150-chickering.pdf
BibTex:
@INPROCEEDINGS{Chickering96,
AUTHOR = "David Chickering ",
TITLE = "Learning Equivalence Classes of Bayesian Networks Structures",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "150--157"
}


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