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Volumn 8, Issue , 2007, Pages 1799-1833

"Ideal parent" structure learning for continuous variable Bayesian networks

Author keywords

Bayesian networks; Continuous variables; Hidden variables; Structure learning

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING;

EID: 34548183010     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (33)

References (30)
  • 1
    • 0001019707 scopus 로고    scopus 로고
    • Learning Bayesian networks is NP-complete
    • D. Fisher and H. J. Lenz, editors, Springer-Verlag, New York
    • D. M. Chickering. Learning Bayesian networks is NP-complete. In D. Fisher and H. J. Lenz, editors, Learning from Data: Artificial Intelligence and Statistics V, pages 121-130. Springer-Verlag, New York, 1996a.
    • (1996) Learning from Data: Artificial Intelligence and Statistics V , pp. 121-130
    • Chickering, D.M.1
  • 2
    • 0002332440 scopus 로고    scopus 로고
    • Learning equivalence classes of Bayesian network structures
    • E. Horvitz and F. Jensen, editors, San Francisco, Morgan Kaufmann
    • D. M. Chickering. Learning equivalence classes of Bayesian network structures. In E. Horvitz and F. Jensen, editors, Proc. Twelfth Conference on Uncertainty in Artificial Intelligence (UAI '96), pages 150-157, San Francisco, 1996b. Morgan Kaufmann.
    • (1996) Proc. Twelfth Conference on Uncertainty in Artificial Intelligence (UAI '96) , pp. 150-157
    • Chickering, D.M.1
  • 7
    • 84898950733 scopus 로고    scopus 로고
    • Discovering hidden variables: A structure-based approach
    • T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Cambridge, Mass, MIT Press
    • G. Elidan, N. Lotner, N. Friedman, and D. Koller. Discovering hidden variables: A structure-based approach. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 479-485, Cambridge, Mass., 2001. MIT Press.
    • (2001) Advances in Neural Information Processing Systems 13 , pp. 479-485
    • Elidan, G.1    Lotner, N.2    Friedman, N.3    Koller, D.4
  • 8
    • 0001586968 scopus 로고    scopus 로고
    • Learning belief networks in the presence of missing values and hidden variables
    • D. Fisher, editor, Morgan Kaufmann, San Francisco
    • N. Friedman. Learning belief networks in the presence of missing values and hidden variables. In D. Fisher, editor, Proc. Fourteenth International Conference on Machine Learning, pages 125-133. Morgan Kaufmann, San Francisco, 1997.
    • (1997) Proc. Fourteenth International Conference on Machine Learning , pp. 125-133
    • Friedman, N.1
  • 10
    • 0002219642 scopus 로고    scopus 로고
    • Learning Bayesian network structure from massive data sets: The 'sparse candidate algorithm
    • K. Laskey and H. Prade, editors, San Francisco
    • N. Friedman, I. Nachman, and D. Pe'er. Learning Bayesian network structure from massive data sets: The 'sparse candidate" algorithm. In K. Laskey and H. Prade, editors, Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99), page 206-215, San Francisco, 1999.
    • (1999) Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99) , pp. 206-215
    • Friedman, N.1    Nachman, I.2    Pe'er, D.3
  • 14
    • 0000935895 scopus 로고    scopus 로고
    • An introduction to variational approximations methods for graphical models
    • M. I. Jordan, editor, Kluwer, Dordrecht, Netherlands
    • M. I. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul. An introduction to variational approximations methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, Dordrecht, Netherlands, 1998.
    • (1998) Learning in Graphical Models
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.K.4
  • 15
    • 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
  • 16
    • 0002480085 scopus 로고
    • Graphical models for associations between variables, some of which are qualitative and some quantitative
    • S. L. Lauritzen and N. Wermuth. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17:31-57, 1989.
    • (1989) Annals of Statistics , vol.17 , pp. 31-57
    • Lauritzen, S.L.1    Wermuth, N.2
  • 17
    • 0004089936 scopus 로고
    • Discrete factor analysis: Learning hidden variables in Bayesian networks
    • Technical report, Department of Computer Science, University of Pittsburgh
    • J. Martin and K. VanLehn. Discrete factor analysis: Learning hidden variables in Bayesian networks. Technical report, Department of Computer Science, University of Pittsburgh, 1995.
    • (1995)
    • Martin, J.1    VanLehn, K.2
  • 19
    • 1942452317 scopus 로고    scopus 로고
    • Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning
    • T. Fawcett and N. Mishra, editors, Menlo Park, California
    • A. Moore and W. Wong. Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning. In T. Fawcett and N. Mishra, editors, Proceedings of the 20th International Conference on Machine Learning (ICML '03), pages 552-559, Menlo Park, California, 2003.
    • (2003) Proceedings of the 20th International Conference on Machine Learning (ICML '03) , pp. 552-559
    • Moore, A.1    Wong, W.2
  • 20
    • 0002425879 scopus 로고    scopus 로고
    • Loopy belief propagation for approximate inference: An empirical study
    • K. Laskey and H. Prade, editors, San Francisco, Morgan Kaufmann
    • K. Murphy and Y. Weiss. Loopy belief propagation for approximate inference: An empirical study. In K. Laskey and H. Prade, editors, Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99), page 467-475, San Francisco, 1999. Morgan Kaufmann.
    • (1999) Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99) , pp. 467-475
    • Murphy, K.1    Weiss, Y.2
  • 21
    • 14844307159 scopus 로고    scopus 로고
    • Inferring quantitative models of regulatory networks from expression data
    • I. Nachman, A. Regev, and N. Friedman. Inferring quantitative models of regulatory networks from expression data. Bioinformatics, 20(Suppl 1):S1248-1256, 2004.
    • (2004) Bioinformatics , vol.20 , Issue.SUPPL. 1
    • Nachman, I.1    Regev, A.2    Friedman, N.3
  • 22
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • G. Schwarz. Estimating the dimension of a model. Annals of Statistics, 6:461-464, 1978.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 24
    • 80053201441 scopus 로고    scopus 로고
    • A simple approach for finding the globally optimal Bayesian network structure
    • Dechter and Richardson, editors, San Francisco, Morgan Kaufmann
    • T. Silander and P. Myllym. A simple approach for finding the globally optimal Bayesian network structure. In Dechter and Richardson, editors, Proc. Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI '06), San Francisco, 2006. Morgan Kaufmann.
    • (2006) Proc. Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI '06)
    • Silander, T.1    Myllym, P.2
  • 25
    • 34548151699 scopus 로고    scopus 로고
    • Finding optimal Bayesian networks by dynamic programming
    • Technical report, Carnegie Mellon University
    • A. Singh and A. Moore. Finding optimal Bayesian networks by dynamic programming. Technical report, Carnegie Mellon University, 2005.
    • (2005)
    • Singh, A.1    Moore, A.2
  • 27
    • 36348929435 scopus 로고    scopus 로고
    • Ordering-based search: A simple and effective algorithm for learning Bayesian networks
    • F. Bacchus and T. Jaakkola, editors, San Francisco, Morgan Kaufmann
    • M. Teyssier and D. Koller. Ordering-based search: A simple and effective algorithm for learning Bayesian networks. In F. Bacchus and T. Jaakkola, editors, Proc. Twenty First Conference on Uncertainty in Artificial Intelligence (UAI '05), pages 584-590, San Francisco, 2005. Morgan Kaufmann.
    • (2005) Proc. Twenty First Conference on Uncertainty in Artificial Intelligence (UAI '05) , pp. 584-590
    • Teyssier, M.1    Koller, D.2
  • 30
    • 21844479166 scopus 로고    scopus 로고
    • Hierarchical latent class models for cluster analysis
    • N.L. Zhang. Hierarchical latent class models for cluster analysis. Journal of Machine Learning Research, 5:697-723, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 697-723
    • Zhang, N.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.