Uncertainty in Artificial Intelligence
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Finding Optimal Bayesian Network Structures with Constraints Learned from Data
Xiannian Fan, Brandon Malone, Changhe Yuan
Several recent algorithms for learning Bayesian network structures first calculate potentially op- timal parent sets (POPS) for all variables and then use various optimization techniques to find a set of POPS, one for each variable, that con- stitutes an optimal network structure. This pa- per makes the observation that there is useful information implicit in the POPS. Specifically, the POPS of a variable constrain its parent can- didates. Moreover, the parent candidates of all variables together give a directed cyclic graph, which often decomposes into a set of strongly connected components (SCCs). Each SCC cor- responds to a smaller subproblem which can be solved independently of the others. Our results show that solving the constrained subproblems significantly improves the efficiency and scala- bility of heuristic search-based structure learning algorithms. Further, we show that by consider- ing only the top p POPS of each variable, we quickly find provably very high quality networks for large datasets.
Pages: 200-209
PS Link:
PDF Link: /papers/14/p200-fan.pdf
AUTHOR = "Xiannian Fan and Brandon Malone and Changhe Yuan",
TITLE = "Finding Optimal Bayesian Network Structures with Constraints Learned from Data",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "200--209"

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