A simple approach for finding the globally optimal Bayesian network structure

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 445-452

  Silander, Tomi ^a   Myllymäki, Petri ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORK STRUCTURE; NP-HARD; SIMPLE APPROACH; STATE-OF-THE-ART ALGORITHMS; VARIABLE ORDERING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS;

STRUCTURAL OPTIMIZATION;

EID: 80053201441     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Chickering, D. (1995). A transformational characterization of equivalent Bayesian network structures. In Proceedings of the 11th annual conference on uncertainty in artificial intelligence (UAI-95) (p. 87-98). San Francisco, CA: Morgan Kaufmann Publishers.
2. Chickering, D., Meek, C., & Heckerman, D. (2003). Large-sample learning of Bayesian networks is NP-hard. In Proceedings of the 19th annual conference on uncertainty in artificial intelligence (UAI-03) (p. 124-133). San Francisco, CA: Morgan Kaufmann Publishers.
3. Heckerman, D. (1996). A tutorial on learning with Bayesian networks (Tech. Rep. No. MSR-TR-95-06). One Microsoft Way, Redmond, WA 98052: Microsoft Research, Advanced Technology Division.
4. Koivisto, M., & Sood, K. (2004). Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res., 5, 549-573.
5. Singh, A., & Moore, A. (2005). Finding optimal Bayesian networks by dynamic programming (Tech. Rep.). Carnegie Mellon University.