Learning Bayesian network models from incomplete data using importance sampling

AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

Volumn , Issue , 2005, Pages 301-308

  Riggelsen, Carsten ^a   Feelders, Ad ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; CORONARY HEART DISEASE; EXACT INFERENCE; IMPUTATION METHODS; INCOMPLETE DATA; LEARNING BAYESIAN NETWORKS; MISSING DATA; OBSERVED DATA; POSTERIOR DISTRIBUTIONS; PREDICTIVE DISTRIBUTIONS; RISK FACTORS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HEURISTIC METHODS; IMPORTANCE SAMPLING;

BAYESIAN NETWORKS;

EID: 33745454493     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. N. Angelopoulos and J. Cussens. Markov chain Monte Carlo using trees-based priors on model structure. In J. Breese and D. Koller, editors, Proc. of the Conf. on Uncertainty in AI, pages 16-23, 2001.
2. D. Chickering. A transformational characterization of equivalent Bayesian networks. In P. Besnard and S. Hanks, editors, Proc. of the Conf. on Uncertainty in AI, pages 87-98, 1995.
3. G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309-347, 1992.
4. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society, Series B, 34:1-38, 1977.
5. D. Edwards and T. Havránek. A fast procedure for model search in multidimensional contingency tables. Biometrika, 72(2):339-351, 1985.
6. 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.
7. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(6), 1984.
8. P. Giudici and R. Castelo. Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 50(1):127-158, 2003.
9. P. Giudici and P. Green. Decomposable graphical gaussian model determination. Biometrika, 86(4): 785-801, 1999.
10. 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.
11. S. L. Lauritzen. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19:191-201, 1995.
12. R. J. Little and D. B. Rubin. Statistical Analysis with Missing Data. Wiley and Sons, 1987.
13. D. Madigan and J. York. Bayesian graphical models for discrete data. Intl. Statistical Review, 63:215-232, 1995.
14. G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley, 1997.
15. 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.
16. M. Ramoni and P. Sebastiani. Robust learning with missing data. Machine Learning, 45(2):147-170, 2001.
17. D. J. Spiegelhalter and S. L. Lauritzen. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579-605, 1990.
18. M. Tanner and W. Wong. The calculation of posterior distributions by data augmentation. J. of the Am. Stat. Assoc., 82(398):528-540, 1987.