Constrained likelihood for reconstructing a directed acyclic Gaussian graph.

ABSTRACT: Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. We propose a novel approach to simultaneously identifying all estimable directed edges and model parameters, using constrained maximum likelihood with nonconvex constraints. We develop a constraint reduction m

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC6373419 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature