New skeleton-based approaches for Bayesian structure learning of Bayesian networks

• Published in: Applied soft computing 2013-02, Vol.13 (2), p.1110-1120
• Main Authors: Masegosa, Andrés R., Moral, Serafín
• Format: Article
• Language: English
• Subjects: Bayesian networks; Bayesian structure learning; Markov chain Monte Carlo; Probabilistic graphical models; Stochastic search
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2012.09.029

[Display omitted] ► Bayesian structure learning involves a sum over a super-exponential number of Bayesian networks. ► We introduce new skeleton-based approaches for addressing this problem. ► Undirected graph skeletons are built based on efficient local search methods. ► Our methods use these graph skeletons to constrain the model space. ► The presented approaches are efficient and provide competitive approximations. Automatically learning the graph structure of a single Bayesian network (BN) which accurately represents the underlying multivariate probability distribution of a collection of random variables is a challenging task. But obtaining a Bayesian solution to this problem based on computing the posterior probability of the presence of any edge or any directed path between two variables or any other structural feature is a much more involved problem, since it requires averaging over all the possible graph structures. For the former problem, recent advances have shown that search+score approaches find much more accurate structures if the search is constrained by a previously inferred skeleton (i.e. a relaxed structure with undirected edges which can be inferred using local search based methods). Based on similar ideas, we propose two novel skeleton-based approaches to approximate a Bayesian solution to the BN learning problem: a new stochastic search which tries to find directed acyclic graph (DAG) structures with a non-negligible score; and a new Markov chain Monte Carlo method over the DAG space. These two approaches are based on the same idea. In a first step, both employ a previously given skeleton and build a Bayesian solution constrained by this skeleton. In a second step, using the preliminary solution, they try to obtain a new Bayesian approximation but this time in an unconstrained graph space, which is the final outcome of the methods. As shown in the experimental evaluation, this new approach strongly boosts the performance of these two standard techniques proving that the idea of employing a skeleton to constrain the model space is also a successful strategy for performing Bayesian structure learning of BNs.