Learning Bayesian network structure using Markov blanket decomposition
SCIE
SCOPUS
- Title
- Learning Bayesian network structure using Markov blanket decomposition
- Authors
- Bui, AT; Jun, CH
- Date Issued
- 2012-12-01
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- Causal structure learning algorithms construct Bayesian networks from observational data. Using non-interventional data, existing constraint-based algorithms may return I-equivalent partially directed acyclic graphs. However, these algorithms do not fully exploit the graphical properties of Bayesian networks, and require many redundant tests that reduce both speed and accuracy. In this paper, we introduce ideas to exploit such properties to increase the speed and accuracy of causal structure learning for multivariate normal data. In numerical experiments on five benchmarking networks our proposed algorithm was faster and more accurate than recently-developed algorithms. (c) 2012 Elsevier B.V. All rights reserved.
- Keywords
- Causal structure learning; Conditional independence test; Directed acyclic graph; Directed global Markov property; Moral graph; V structure; CAUSAL DISCOVERY; MODEL
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/15740
- DOI
- 10.1016/J.PATREC.2012.06.013
- ISSN
- 0167-8655
- Article Type
- Article
- Citation
- PATTERN RECOGNITION LETTERS, vol. 33, no. 16, page. 2134 - 2140, 2012-12-01
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.