Efficient algorithms for conditional independence inference

0353652 - ÚTIA 2011 RIV US eng J - Článek v odborném periodiku
Bouckaert, R. - Hemmecke, R. - Lindner, S. - Studený, Milan
Efficient algorithms for conditional independence inference.
Journal of Machine Learning Research. Roč. 11, č. 1 (2010), s. 3453-3479. ISSN 1532-4435
Grant CEP: GA ČR GA201/08/0539; GA MŠMT 1M0572
Výzkumný záměr: CEZ:AV0Z10750506
Klíčová slova: conditional independence inference * linear programming approach
Kód oboru RIV: BA - Obecná matematika
Impakt faktor: 2.949, rok: 2010
http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf

The topic of the paper is computer testing of (probabilistic) conditional independence (CI) implications by an algebraic method of structural imsets. The basic idea is to transform CI statements into certain integral vectors and to verify by a computer the corresponding algebraic relation between the vectors, called the independence implication. The main contribution of the paper is a new method, based on linear programming (LP), which overcomes the limitation of former methods to the number of involved variables. The computational experiments, described in the paper, also show that the new method is faster than the previous ones.
Trvalý link: http://hdl.handle.net/11104/0192831