A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
نویسنده
چکیده
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. Prelimi nary 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 per formance of suboptimal heuristic search algo rithms and the adequacy of the MDL princi ple in learning Bayesian networks.
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