Abstract. Various different algorithms for learning Bayesian networks from data have been proposed to date. In this paper, we adopt a novel approach that combines the main advantages of these algorithms yet avoids their difficulties. In our approach, first an undirected graph, termed the skeleton, is constructed from the data, using zero-and first-order dependence tests. Then, a search algorithm is employed that builds upon a quality measure to find the best network from the search space that is defined by the skeleton. To corroborate the feasibility of our approach, we present the experimental results that we obtained on various different datasets generated from real-world networks. Within the experimental setting, we further study the reduction of the search space that is achieved by the skeleton.
There also exists global search methods in which the search space is also restricted, for example in REF they first construct an undirected graph or skeleton by using zero-and first-order dependence tests, and then a genetic algorithm is employed which is restricted to searching for DAGs belonging to this skeleton.
A Skeleton-Based Approach to Learning Bayesian Networks From Data
of Lecture
Computer Science