Exact estimation of multiple directed acyclic graphs

Oates, CJ; Smith, JQ; Mukherjee, S; Cussens, J

Exact estimation of multiple directed acyclic graphs

Oates, CJ

Smith, JQ Mukherjee, S Cussens, J

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Publication Type:: Journal Article
Citation:: Statistics and Computing, 2016, 26 (4), pp. 797 - 811
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Oates, CJ https://orcid.org/0000-0002-4444-8603	en_US
dc.contributor.author	Smith, JQ	en_US
dc.contributor.author	Mukherjee, S	en_US
dc.contributor.author	Cussens, J	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	Statistics and Computing, 2016, 26 (4), pp. 797 - 811	en_US
dc.identifier.issn	0960-3174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/88323
dc.description.abstract	© 2015, Springer Science+Business Media New York. This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.	en_US
dc.relation.ispartof	Statistics and Computing	en_US
dc.relation.isbasedon	10.1007/s11222-015-9570-9	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Exact estimation of multiple directed acyclic graphs	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	26	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2015, Springer Science+Business Media New York. This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.

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http://hdl.handle.net/10453/88323