-
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
-
3
-
-
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
-
4
-
-
0037262841
-
Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
-
N. Friedman and D. 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
-
5
-
-
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
-
6
-
-
0001099335
-
Decomposable graphical gaussian model determination
-
P. Giudici and P. Green. Decomposable graphical gaussian model determination. Biometrika, 86(4):785-801, 1999.
-
(1999)
Biometrika
, vol.86
, Issue.4
, pp. 785-801
-
-
Giudici, P.1
Green, P.2
-
7
-
-
77956889087
-
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
-
P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82:711-732, 1998.
-
(1998)
Biometrika
, vol.82
, pp. 711-732
-
-
Green, P.1
-
8
-
-
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
-
9
-
-
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
-
10
-
-
84950945692
-
Model selection and accounting for model uncertainty in graphical models using Occam's window
-
D. Madigan and A. Raftery. Model selection and accounting for model uncertainty in graphical models using Occam's window. J. of the Am. Stat. Assoc., 89:1535-1546, 1994.
-
(1994)
J. of the Am. Stat. Assoc.
, vol.89
, pp. 1535-1546
-
-
Madigan, D.1
Raftery, A.2
-
11
-
-
21844520724
-
Bayesian graphical models for discrete data
-
D. Madigan and J. York. Bayesian graphical models for discrete data. Intl. Statistical Review, 63:215-232, 1995.
-
(1995)
Intl. Statistical Review
, vol.63
, pp. 215-232
-
-
Madigan, D.1
York, J.2
-
13
-
-
84986980101
-
Sequential updating of conditional probabilities on directed graphical structures
-
D. J. Spiegelhalter and S. L. Lauritzen. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579-605, 1990.
-
(1990)
Networks
, vol.20
, pp. 579-605
-
-
Spiegelhalter, D.J.1
Lauritzen, S.L.2
|