Structural Learning of Chain Graphs via Decomposition
Files
Penn collection
Degree type
Discipline
Subject
conditional independence
decomposition
graphical model
structural learning
Computer Sciences
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Contributor
Abstract
Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the idea of decomposing the learning problem into a set of smaller scale problems on its decomposed subgraphs. The decomposition requires conditional independencies but does not require the separators to be complete subgraphs. Algorithms for both skeleton recovery and complex arrow orientation are presented. Simulations under a variety of settings demonstrate the competitive performance of our method, especially when the underlying graph is sparse.