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Volumn 31, Issue , 2013, Pages 370-378

Exact learning of bounded tree-width Bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; TREES (MATHEMATICS);

EID: 84919758182     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (37)

References (27)
  • 2
    • 0001294529 scopus 로고    scopus 로고
    • A linear time algorithm for finding tree-decompositions of small treewidth
    • H. L. Bodlaender. A linear time algorithm for finding tree-decompositions of small treewidth. SIAM Journal of Computing, 25: 1305-1317, 1996.
    • (1996) SIAM Journal of Computing , vol.25 , pp. 1305-1317
    • Bodlaender, H.L.1
  • 6
    • 84933530882 scopus 로고
    • Approximating discrete probability distributions with dependence trees
    • C. K. Chow and C. N. Liu. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14(3): 462-467, 1968.
    • (1968) IEEE Transactions on Information Theory , vol.14 , Issue.3 , pp. 462-467
    • Chow, C.K.1    Liu, C.N.2
  • 7
    • 0025401005 scopus 로고
    • The computational complexity of probabilistic inference using Bayesian belief networks
    • G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42: 393-405, 1990.
    • (1990) Artificial Intelligence , vol.42 , pp. 393-405
    • Cooper, G.F.1
  • 8
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9: 309-347, 1992.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 11
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3): 197-243, 1995.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 13
    • 77958543971 scopus 로고    scopus 로고
    • Respecting Markov equivalence in computing posterior probabilities of causal graphical features
    • AAAI Press
    • E. Y. Kang, I. Shpitser, and E. Eskin. Respecting Markov equivalence in computing posterior probabilities of causal graphical features. In 24th AAAI Conference on Artificial Intelligence (AAAI), pages 1175-1180. AAAI Press, 2010.
    • (2010) 24th AAAI Conference on Artificial Intelligence (AAAI , pp. 1175-1180
    • Kang, E.Y.1    Shpitser, I.2    Eskin, E.3
  • 17
    • 31844439894 scopus 로고    scopus 로고
    • Exact Bayesian structure discovery in Bayesian networks
    • M. Koivisto and K. Sood. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 5: 549-573, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 549-573
    • Koivisto, M.1    Sood, K.2
  • 19
    • 13444275705 scopus 로고    scopus 로고
    • Finding optimal gene networks using biological constraints
    • S. Ott and S. Miyano. Finding optimal gene networks using biological constraints. Genome Informatics, 14: 124-133, 2003.
    • (2003) Genome Informatics , vol.14 , pp. 124-133
    • Ott, S.1    Miyano, S.2
  • 26
    • 80053142920 scopus 로고    scopus 로고
    • Computing posterior probabilities of structural features in Bayesian networks
    • AUAI Press
    • J. Tian and R. He. Computing posterior probabilities of structural features in Bayesian networks. In 25th Conference on Uncertainty in Artificial Intelligence (UAI), pages 538-547. AUAI Press, 2009.
    • (2009) 25th Conference on Uncertainty in Artificial Intelligence (UAI , pp. 538-547
    • Tian, J.1    He, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.