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Abstract Details

Activity Number: 285
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306896
Title: Maximum Likelihood Estimation Over Directed Acyclic Gaussian Graphs
Author(s): Yiping Yuan*+
Companies: University of Minnesota
Address: School of Statistics, , ,
Keywords: Multiple directed acyclic graphs ; Maximum likelihood ; Non-convex constraints ; Pairwise coordinate descent

Directed acyclic graphs(DAGs) are useful to represent causal relations among random variables. Estimation of DAGs in multiple graphical models becomes challenging in the presence of inhomogeneous data. In this article, we estimate multiple DAGs from Gaussian graphical models with a known partial ordering, where inferring causality between variables is of particular interest, through identifying the sparseness structure, with efforts focused towards detection of structural change over adjacency matrices of DAGs. For maximum likelihood estimation, we introduce non-convex constraints on the elements as well as element-wise differences of adjacency matrices. Computationally, we introduce an efficient algorithm based on the augmented Lagrange multipliers, difference convex method and a novel fast algorithm for solving a convex relaxed problem. Numerical results illustrate that the proposed method performs well against its alternatives.

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