메뉴 건너뛰기




Volumn , Issue , 2009, Pages 538-547

Computing posterior probabilities of structural features in Bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; PROBABILITY;

EID: 80053142920     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (32)

References (15)
  • 2
    • 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
  • 4
    • 49549100459 scopus 로고    scopus 로고
    • Learning causal Bayesian network structures from experimental data
    • B. Ellis and W. H. Wong. Learning causal Bayesian network structures from experimental data. J. Am. Stat. Assoc., 103:778-789, 2008.
    • (2008) J. Am. Stat. Assoc. , vol.103 , pp. 778-789
    • Ellis, B.1    Wong, W.H.2
  • 5
    • 0037262841 scopus 로고    scopus 로고
    • Being bayesian about network structure: A bayesian approach to structure discovery in bayesian networks
    • Nir Friedman and Daphne Koller. Being bayesian about network structure: A bayesian approach to structure discovery in bayesian networks. Machine Learning, 50(1-2):95-125, 2003.
    • (2003) Machine Learning , vol.50 , Issue.1-2 , pp. 95-125
    • Friedman, N.1    Koller, D.2
  • 6
    • 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, 20:197-243, 1995.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 7
    • 0012315692 scopus 로고    scopus 로고
    • A Bayesian approach to causal discovery
    • Glymour C. and Cooper G.F., editors, Menlo Park, CA. AAAI Press and MIT Press
    • D. Heckerman, C. Meek, and G. Cooper. A Bayesian approach to causal discovery. In Glymour C. and Cooper G.F., editors, Computation, Causation, and Discovery, Menlo Park, CA, 1999. AAAI Press and MIT Press.
    • (1999) Computation, Causation, and Discovery
    • Heckerman, D.1    Meek, C.2    Cooper, G.3
  • 8
    • 0000252076 scopus 로고
    • Computational aspects of the mobius transformation
    • P. B. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors
    • R. Kennes and P. Smets. Computational aspects of the mobius transformation. In P. B. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pages 401-416, 1990.
    • (1990) Proceedings of the Conference on Uncertainty in Artificial Intelligence , pp. 401-416
    • Kennes, R.1    Smets, P.2
  • 9
    • 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
  • 11


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.