Maximum Likelihood Estimation Over Directed Acyclic Gaussian Graphs.
Maximum Likelihood Estimation Over Directed Acyclic Gaussian Graphs.
复制标题
有向非循环高斯图的最大似然估计。
DOI：
10.1002/sam.11168
复制
发表时间：
2012
影响因子：
1.3
通讯作者：
Pan,Wei
中科院分区：
文献类型：
--
作者：
Yuan,Yiping;Shen,Xiaotong;Pan,Wei
在存在非均匀数据的情况下，多有向图的估计变得具有挑战性，其中有向无环图(DAG)被用来表示随机变量之间的因果关系。为了推断变量之间的因果关系，我们估计了给定高斯图形模型中已知顺序的多个DAG。特别地，我们提出了一种元素上的非凸约束和邻接矩阵的元素差的约束极大似然方法，用于识别图的稀疏结构以及检测邻接矩阵上的结构变化。在计算上，我们提出了一种基于增广拉格朗日乘子的高效算法、差分凸方法和一种新的求解凸弛豫子问题的快速算法。数值结果表明，对于模拟数据和真实数据，该方法的性能优于其他方法。统计分析和数据挖掘2011DOI：10.1002/sam.11168
Estimation of multiple directed graphs becomes challenging in the presence of inhomogeneous data, where directed acyclic graphs (DAGs) are used to represent causal relations among random variables. To infer causal relations among variables, we estimate multiple DAGs given a known ordering in Gaussian graphical models. In particular, we propose a constrained maximum likelihood method with nonconvex constraints over elements and element‐wise differences of adjacency matrices, for identifying the sparseness structure as well as detecting structural changes over adjacency matrices of the graphs. Computationally, we develop an efficient algorithm based on augmented Lagrange multipliers, the difference convex method, and a novel fast algorithm for solving convex relaxation subproblems. Numerical results suggest that the proposed method performs well against its alternatives for simulated and real data.Statistical Analysis and Data Mining2011 DOI: 10.1002/sam.11168