-
1
-
-
0002460150
-
The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks
-
I.A. Beinlich, H.J. Suermondt, R.M. Chavez, and G.F. Cooper. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proc. of the European Conf., on AI in Medicine, 1989.
-
(1989)
Proc. of the European Conf., on AI in Medicine
-
-
Beinlich, I.A.1
Suermondt, H.J.2
Chavez, R.M.3
Cooper, G.F.4
-
2
-
-
0031273462
-
Adaptive probabilistic networks with hidden variables
-
J. Binder, D. Koller, S. J. Russell, and K. Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning, 29:213-244, 1997.
-
(1997)
Machine Learning
, vol.29
, pp. 213-244
-
-
Binder, J.1
Koller, D.2
Russell, S.J.3
Kanazawa, K.4
-
4
-
-
0001926525
-
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
-
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
-
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
-
7
-
-
0002629270
-
Maximum likelihood from incomplete data via the em algorithm
-
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.
-
(1977)
J. of the Royal Statistical Society, Series B
, vol.34
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
8
-
-
0000130823
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
|