On the Use of Skeletons when Learning in Bayesian Networks
This is an incremental improvement for researchers in probabilistic graphical models, focusing on enhancing search strategies in Bayesian network learning.
The paper tackles the problem of learning Bayesian network structures by introducing a heuristic operator that simultaneously optimizes edge orientations using a scoring function, alternating between directed acyclic graphs and skeletons, and shows evaluation on artificial and real-world data.
In this paper, we present a heuristic operator which aims at simultaneously optimizing the orientations of all the edges in an intermediate Bayesian network structure during the search process. This is done by alternating between the space of directed acyclic graphs (DAGs) and the space of skeletons. The found orientations of the edges are based on a scoring function rather than on induced conditional independences. This operator can be used as an extension to commonly employed search strategies. It is evaluated in experiments with artificial and real-world data.