LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS

Mohamed BENDOU, Paul MUNTEANU

2004

Abstract

This paper proposes a new approach for designing learning bayesian network algorithms that explore the structure equivalence classes space. Its main originality consists in the representation of equivalence classes by largest chain graphs, instead of essential graphs which are generally used in the similar task. We show that this approach drastically simplifies the algorithms formulation and has some beneficial aspects on their execution time.

Paper Citation

in Harvard Style

BENDOU M. and MUNTEANU P. (2004). LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 184-190. DOI: 10.5220/0002636301840190

in Bibtex Style

@conference{iceis04,
author={Mohamed BENDOU and Paul MUNTEANU},
title={LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={184-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002636301840190},
isbn={972-8865-00-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS
SN - 972-8865-00-7
AU - BENDOU M.
AU - MUNTEANU P.
PY - 2004
SP - 184
EP - 190
DO - 10.5220/0002636301840190