Bayesian network structure learning by dynamic programming algorithm based on node block sequence constraints

Abstract

The use of dynamic programming (DP) algorithms to learn Bayesian network structures is limited by their high space complexity and difficulty in learning the structure of large-scale networks. Therefore, this study proposes a DP algorithm based on node block sequence constraints. The proposed algorithm constrains the traversal process of the parent graph by using the M-sequence matrix to considerably reduce the time consumption and space complexity by pruning the traversal process of the order graph using the node block sequence. Experimental results show that compared with existing DP algorithms, the proposed algorithm can obtain learning results more efficiently with less than 1% loss of accuracy, and can be used for learning larger-scale networks.