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Volumn 2006, Issue , 2006, Pages 130-140

Learning Bayesian networks from incomplete data: An efficient method for generating approximate predictive distributions

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATA PROCESSING; DATA REDUCTION; ITERATIVE METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SCANNING ELECTRON MICROSCOPY;

EID: 33745464487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972764.12     Document Type: Conference Paper
Times cited : (20)

References (23)
  • 4
    • 0001926525 scopus 로고
    • Theory refinemement on Bayesian networks
    • B. D'Ambrosio, P. Smets, and P. Bonissone, editors
    • W. Buntine. Theory refinemement on Bayesian networks. In B. D'Ambrosio, P. Smets, and P. Bonissone, editors, Proc. of the Conf., on Uncertainty in AI, 1991.
    • (1991) Proc. of the Conf., on Uncertainty in AI
    • Buntine, W.1
  • 5
    • 2542465947 scopus 로고    scopus 로고
    • On inclusion-driven learning of Bayesian networks
    • R. Castelo and T. Kocka. On inclusion-driven learning of Bayesian networks. J. of Machine Learning Research, 4:527-574, 2003.
    • (2003) J. of Machine Learning Research , vol.4 , pp. 527-574
    • Castelo, R.1    Kocka, T.2
  • 6
    • 0012483452 scopus 로고
    • A comparison of sequential learning methods for incomplete data
    • R. G. Cowell, A. P. Dawid, and P. Sebastiani. A comparison of sequential learning methods for incomplete data. Bayesian Statistics, 5:533-541, 1995.
    • (1995) Bayesian Statistics , vol.5 , pp. 533-541
    • Cowell, R.G.1    Dawid, A.P.2    Sebastiani, P.3
  • 8
    • 0000130823 scopus 로고
    • A fast procedure for model search in multidimensional contingency tables
    • D. Edwards and T. Havránek. A fast procedure for model search in multidimensional contingency tables. Biometrika, 72(2):339-351, 1985.
    • (1985) Biometrika , vol.72 , Issue.2 , pp. 339-351
    • Edwards, D.1    Havránek, T.2
  • 9
    • 0001586968 scopus 로고    scopus 로고
    • Learning Bayesian networks in the presence of missing values and hidden variables
    • N. Friedman. Learning Bayesian networks in the presence of missing values and hidden variables. In Intl. Conf., on Machine Learning, pages 125-133, 1997.
    • (1997) Intl. Conf., on Machine Learning , pp. 125-133
    • Friedman, N.1
  • 10
    • 0000854197 scopus 로고    scopus 로고
    • The Bayesian structural em algorithm
    • G. F. Cooper and S. Moral, editors
    • N. Friedman. The Bayesian structural EM algorithm. In G. F. Cooper and S. Moral, editors, Proc. of the Conf., on Uncertainty in AI, pages 129-138, 1998.
    • (1998) Proc. of the Conf., on Uncertainty in AI , pp. 129-138
    • Friedman, N.1
  • 11
    • 0037266163 scopus 로고    scopus 로고
    • Improving Markov chain Monte Carlo model search for data mining
    • P. Giudici and R. Castelo. Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 50(1):127-158, 2003.
    • (2003) Machine Learning , vol.50 , Issue.1 , pp. 127-158
    • Giudici, P.1    Castelo, R.2
  • 12
    • 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
  • 13
    • 0002811779 scopus 로고    scopus 로고
    • Improved learning of Bayesian networks
    • D. Koller and J. Breese, editors
    • T. Kocka and R. Castelo. Improved learning of Bayesian networks. In D. Koller and J. Breese, editors, Proc. of the Conf., on Uncertainty in AI, pages 269-276, 2001.
    • (2001) Proc. of the Conf., on Uncertainty in AI , pp. 269-276
    • Kocka, T.1    Castelo, R.2
  • 14
    • 58149210716 scopus 로고
    • The em algorithm for graphical association models with missing data
    • S. L. Lauritzen. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19:191-201, 1995.
    • (1995) Computational Statistics and Data Analysis , vol.19 , pp. 191-201
    • Lauritzen, S.L.1
  • 18
    • 0002343164 scopus 로고    scopus 로고
    • Learning Bayesian networks from incomplete databases
    • D. Geiger and P. Shenoy, editors
    • M. Ramoni and P. Sebastiani. Learning Bayesian networks from incomplete databases. In D. Geiger and P. Shenoy, editors, Proc. of the Conf., on Uncertainty in AI, pages 401-408, 1997.
    • (1997) Proc. of the Conf., on Uncertainty in AI , pp. 401-408
    • Ramoni, M.1    Sebastiani, P.2
  • 19
    • 0003250080 scopus 로고    scopus 로고
    • Parameter Estimation in Bayesian networks from incomplete databases
    • M. Ramoni and P. Sebastiani. Parameter Estimation in Bayesian networks from incomplete databases. Intelligent Data Analysis Journal, 2(1), 1998.
    • (1998) Intelligent Data Analysis Journal , vol.2 , Issue.1
    • Ramoni, M.1    Sebastiani, P.2
  • 20
    • 33646385607 scopus 로고    scopus 로고
    • MCMC learning of Bayesian network models by Markov blanket decomposition
    • J. Gama, R. Camacho, P. Bazdil, A. Jorge, and L. Torgo, editors
    • C. Riggelsen. MCMC learning of Bayesian network models by Markov blanket decomposition. In J. Gama, R. Camacho, P. Bazdil, A. Jorge, and L. Torgo, editors, European Conf. on Machine Learning, pages 329-340, 2005.
    • (2005) European Conf. on Machine Learning , pp. 329-340
    • Riggelsen, C.1
  • 21
    • 33645980673 scopus 로고    scopus 로고
    • Learning parameters of Bayesian networks from incomplete data via importance sampling
    • To appear
    • C. Riggelsen. Learning parameters of Bayesian networks from incomplete data via importance sampling. Intl. J. of Approximate Reasoning, 2006. To appear.
    • (2006) Intl. J. of Approximate Reasoning
    • Riggelsen, C.1
  • 22
    • 33745454493 scopus 로고    scopus 로고
    • Learning Bayesian network models from incomplete data using importance sampling
    • R. G. Cowell and Z. Ghahramani, editors
    • C. Riggelsen and A. Feelders. Learning Bayesian network models from incomplete data using importance sampling. In R. G. Cowell and Z. Ghahramani, editors, Proc. of Artificial Intelligence and Statistics, pages 301-308, 2005.
    • (2005) Proc. of Artificial Intelligence and Statistics , pp. 301-308
    • Riggelsen, C.1    Feelders, A.2
  • 23
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation
    • M. Tanner and W. Wong. The calculation of posterior distributions by data augmentation. J. of the Am. Stat. Assoc., 82(398):528-540, 1987.
    • (1987) J. of the Am. Stat. Assoc. , vol.82 , Issue.398 , pp. 528-540
    • Tanner, M.1    Wong, W.2


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