1. A computer-implemented method for score computing of a Bayesian network, comprising: receiving, at a structure learning module included in a computing device, a request for a current best structure, the request to include a network neighborhood structure further including a data set of statistical computing information represented as a directed acyclic graph (DAG) with a family structure, where the family structure includes nodes that each represent an observed state, and statistical relationships between nodes in the family structure; computing an intermediate result for a score computation of the family structure of the Bayesian network including a child node, the score computation for structure learning of the Bayesian network, the intermediate result being a score of the family structure resulting from the score computation of the family structure including the child node; caching the intermediate result in a memory device; changing a single edge in the family structure by making a single edge difference between a parent node of the family structure and the child node, wherein the single edge represents a logical relationship between the Parent node and the child node; computing a score of the family structure with the changed single edge using the intermediate result and the single edge difference; comparing the score of the family structure with the changed single edge with a score of another computation of the family structure to determine a higher scoring structure; and outputting the higher scoring structure as a current best structure from the structure learning module.