An inclusion optimal algorithm for chain graph structure learning

Jose Peña, Dag Sonntag, Jens Nielsen
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:778-786, 2014.

Abstract

This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-pena14, title = {{An inclusion optimal algorithm for chain graph structure learning}}, author = {Peña, Jose and Sonntag, Dag and Nielsen, Jens}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {778--786}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/pena14.pdf}, url = {https://proceedings.mlr.press/v33/pena14.html}, abstract = {This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.} }
Endnote
%0 Conference Paper %T An inclusion optimal algorithm for chain graph structure learning %A Jose Peña %A Dag Sonntag %A Jens Nielsen %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-pena14 %I PMLR %P 778--786 %U https://proceedings.mlr.press/v33/pena14.html %V 33 %X This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.
RIS
TY - CPAPER TI - An inclusion optimal algorithm for chain graph structure learning AU - Jose Peña AU - Dag Sonntag AU - Jens Nielsen BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-pena14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 778 EP - 786 L1 - http://proceedings.mlr.press/v33/pena14.pdf UR - https://proceedings.mlr.press/v33/pena14.html AB - This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated. ER -
APA
Peña, J., Sonntag, D. & Nielsen, J.. (2014). An inclusion optimal algorithm for chain graph structure learning. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:778-786 Available from https://proceedings.mlr.press/v33/pena14.html.

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