论文标题
在高斯图形模型中学习块结构化图
Learning block structured graphs in Gaussian graphical models
论文作者
论文摘要
在高斯图形模型的框架内，引入了基础图的先前分布，以在图形的邻接矩阵中诱导块结构，并在固定变量组之间学习关系。在共轭G-Wishart之前，开发了一种名为“双可逆跳跃”马尔特·卡洛（Markov Carlo）的新型抽样策略马尔特·卡洛（Markov Chain Carlo）。该算法提出的移动不仅是添加或删除单个链接，还要删除整个边缘。然后将该方法应用于平滑功能数据。通过在基础膨胀系数上放置图形模型，从而估算了其条件独立性结构，从而改善了经典的平滑过程。由于B型基础基础的元素具有紧凑的支持，因此独立结构反映在定义明确的域的部分。利用功能域的已知分区来研究化合物内物质之间的关系。
Within the framework of Gaussian graphical models, a prior distribution for the underlying graph is introduced to induce a block structure in the adjacency matrix of the graph and learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for block structural learning, under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single link but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional independence structure. Since the elements of a B-Spline basis have compact support, the independence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among the substances within the compound.
