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How matroids occur in the context of learning Bayesian network structure
- 1.0447685 - ÚTIA 2016 RIV US eng C - Conference Paper (international conference)
Studený, Milan
How matroids occur in the context of learning Bayesian network structure.
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-First Conference (2015). Corvallis, Oregon: AUAI Press, 2015, s. 832-841. ISBN 978-0-9966431-0-8.
[31st Conference on Uncertainty in Artificial Intelligence. Amsterdam (NL), 12.07.2015-16.07.2015]
R&D Projects: GA ČR GA13-20012S
Institutional support: RVO:67985556
Keywords : learning Bayesian network structure * matroid * family-variable polytope
Subject RIV: BA - General Mathematics
http://library.utia.cas.cz/separaty/2015/MTR/studeny-0447685.pdf
It is shown that any connected matroid having a non-trivial cluster of BN variables as its ground set induces a facet-defining inequality for the polytope(s) used in the ILP approach to globally optimal BN structure learning. The result applies to well-known k-cluster inequalities, which play a crucial role in the ILP approach.
Permanent Link: http://hdl.handle.net/11104/0249568
Number of the records: 1