Vidaurre Diego, Bielza Concha, Larrañaga Pedro
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660 Madrid, Spain.
IEEE Trans Syst Man Cybern B Cybern. 2010 Oct;40(5):1231-42. doi: 10.1109/TSMCB.2009.2036593. Epub 2010 Jan 15.
Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant.
从数据中学习图形模型的结构是广泛实际应用中的常见任务。在本文中，我们专注于高斯贝叶斯网络，即处理连续数据以及具有由高斯分布给出的所有变量联合概率密度的有向无环图。我们建议在等价类搜索空间中进行工作，具体使用k - 贪心等价搜索算法。这与引导结构搜索的正则化技术相结合，可以学习到接近生成数据的稀疏网络。我们给出了一些合成网络以及对拟南芥植物中调节类异戊二烯生物合成的两条生物学途径的基因网络进行建模的结果。
