A branch-and-bound algorithm for MDL learning Bayesian networks

Authors:
Jin Tian
Affiliations:
Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA
Venue:
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Year:
2000

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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.