论文标题
涡轮宽的贝叶斯网络结构学习
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning
论文作者
论文摘要
我们提出了一种新的方法，用于学习由树宽的贝叶斯网络（BN）的结构。我们方法的关键是在本地应用精确的方法（基于MaxSat），以提高启发式计算的BN的得分。这种方法使我们能够将精确方法的功率扩展（到目前为止仅适用于具有数十个随机变量的BN的BN），以将其扩展到具有数千个随机变量的大型BN。我们的实验表明，我们的方法通常会大大提高最新的启发式方法所提供的BN的得分。
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.
