We present a new approach for learning the structure of a treewidth-bounded Bayesian Network (BN). The key to our approach is applying an exact method (based on MaxSAT) locally, to improve the score of a heuristically computed BN. This approach allows us to scale the power of exact methods -- so far only applicable to BNs with several dozens of random variables -- to large BNs with several thousands of random variables. Our experiments show that our method improves the score of BNs provided by state-of-the-art heuristic methods, often significantly.

翻译：我们提出了一种新的方法来学习植树的海湾网络(BN)的结构。我们的方法的关键在于在当地应用一种精确的方法(基于MaxSAT)来改进超常计算BN的分数。这种方法使我们能够将精确方法的力量(迄今为止只适用于有几十个随机变量的BN)扩大到有数千个随机变量的大的生物网。我们的实验表明,我们的方法提高了由最先进的超常方法提供的BN的分数,通常非常显著。