Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

Abstract

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.

Learning of Bounded Treewidth Bayesian Networks via A-kg

“…The method formulated the learning Bayesian network as a shortest path-finding problem and used an A* search algorithm to approach the problem [3]. Di proposed a BN structure learning algorithm based on dynamic programming, which integrated improved MMPC and MWST [4]. In the A* search algorithm, Wang improved the simple heuristic and the static k-cycle conflict heuristic to adapt to ancestral constraints [5].…”