-
1
-
-
25444464759
-
Computing linear extensions is #P-complete
-
G. Brightwell and P. Winkler. Computing linear extensions is #P-complete. In STOC, 1991.
-
(1991)
STOC
-
-
Brightwell, G.1
Winkler, P.2
-
2
-
-
84933530882
-
Approximating discrete probability distributions with dependence trees
-
C. K. Chow and C. N. Liu. Approximating discrete probability distributions with dependence trees. IEEE Trans. on Info. Theory, 14:462-67, 1968.
-
(1968)
IEEE Trans. on Info. Theory
, vol.14
, pp. 462-467
-
-
Chow, C.K.1
Liu, C.N.2
-
3
-
-
0038517651
-
Finding optimal Bayesian networks
-
D. Chickering and C. Meek. Finding Optimal Bayesian Networks. In UAI, 2002.
-
(2002)
UAI
-
-
Chickering, D.1
Meek, C.2
-
4
-
-
0007047929
-
Causal discovery from a mixture of experimental and observational data
-
G. Cooper and C. Yoo. Causal discovery from a mixture of experimental and observational data. In UAI, 1999.
-
(1999)
UAI
-
-
Cooper, G.1
Yoo, C.2
-
5
-
-
29144531645
-
Model averaging for prediction with discrete Bayesian networks
-
D. Dash and G. Cooper. Model Averaging for Prediction with Discrete Bayesian Networks. J. of Machine Learning Research, 5:1177-1203, 2004.
-
(2004)
J. of Machine Learning Research
, vol.5
, pp. 1177-1203
-
-
Dash, D.1
Cooper, G.2
-
8
-
-
70349363064
-
Sampling Bayesian networks quickly
-
B. Ellis and W. Wong. Sampling Bayesian Networks quickly. In Interface, 2006.
-
(2006)
Interface
-
-
Ellis, B.1
Wong, W.2
-
9
-
-
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, 50:95-126, 2003.
-
(2003)
Machine Learning
, vol.50
, pp. 95-126
-
-
Friedman, N.1
Koller, D.2
-
10
-
-
0000854197
-
Learning the structure of dynamic probabilistic networks
-
N. Friedman, K. Murphy, and S. Russell. Learning the structure of dynamic probabilistic networks. In UAI, 1998.
-
(1998)
UAI
-
-
Friedman, N.1
Murphy, K.2
Russell, S.3
-
11
-
-
0037266163
-
Improving Markov chain Monte Carlo model search for data mining
-
January
-
P Giudici and R. Castelo. Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 50(1-2):127 - 158, January 2003.
-
(2003)
Machine Learning
, vol.50
, Issue.1-2
, 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 M. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20(3):197-243, 1995.
-
(1995)
Machine Learning
, vol.20
, Issue.3
, pp. 197-243
-
-
Heckerman, D.1
Geiger, D.2
Chickering, M.3
-
13
-
-
0344464762
-
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
-
DOI 10.1093/bioinformatics/btg313
-
D. Husmeier. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19:2271-2282, 2003. (Pubitemid 37483983)
-
(2003)
Bioinformatics
, vol.19
, Issue.17
, pp. 2271-2282
-
-
Husmeier, D.1
-
14
-
-
29144477220
-
Bayesian model averaging of Bayesian network classifiers over multiple node-orders: Application to sparse datasets
-
DOI 10.1109/TSMCB.2005.850162
-
K.-B. Hwang and B.-T. Zhang. Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets. IEEE Trans. on Systems, Man and Cybernetics, 35(6):1302-1310, 2005. (Pubitemid 41800428)
-
(2005)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
, vol.35
, Issue.6
, pp. 1302-1310
-
-
Hwang, K.-B.1
Zhang, B.-T.2
-
15
-
-
69249216907
-
Advances in exact Bayesian structure discovery in Bayesian networks
-
M. Koivisto. Advances in exact Bayesian structure discovery in Bayesian networks. In UAI, 2006.
-
(2006)
UAI
-
-
Koivisto, M.1
-
16
-
-
31844439894
-
Exact Bayesian structure discovery in Bayesian networks
-
M. Koivisto and K. Sood. Exact Bayesian structure discovery in Bayesian networks. J. of Machine Learning Research, 5:549-573, 2004.
-
(2004)
J. of Machine Learning Research
, vol.5
, pp. 549-573
-
-
Koivisto, M.1
Sood, K.2
-
19
-
-
33644516911
-
Tractable Bayesian learning of tree belief networks
-
DOI 10.1007/s11222-006-5535-3
-
M. Meila and T. Jaakkola. Tractable Bayesian learning of tree belief networks. Statistics and Computing, 16:77-92, 2006. (Pubitemid 43296619)
-
(2006)
Statistics and Computing
, vol.16
, Issue.1
, pp. 77-92
-
-
Meila, M.1
Jaakkola, T.2
-
21
-
-
0001828003
-
Cached sufficient statistics for efficient machine learning with large datasets
-
Andrew W Moore and Mary S. Lee. Cached sufficient statistics for efficient machine learning with large datasets. J. of AI Research, 8:67-91, 1998.
-
(1998)
J. of AI Research
, vol.8
, pp. 67-91
-
-
Moore, A.W.1
Lee, M.S.2
-
22
-
-
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
-
23
-
-
1942452317
-
Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning
-
Andrew Moore and Weng-Keen Wong. Optimal reinsertion: A new search operator for accelerated and more accurate bayesian network structure learning. In Intl. Conf. on Machine Learning, pages 552-559, 2003.
-
(2003)
Intl. Conf. on Machine Learning
, pp. 552-559
-
-
Moore, A.1
Wong, W.-K.2
-
24
-
-
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
-
26
-
-
0001457227
-
Counting labeled acyclic digraphs
-
F. Harary, editor Academic Press
-
R. W. Robinson. Counting labeled acyclic digraphs. In F. Harary, editor, New Directions in the Theory of Graphs, pages 239-273. Academic Press, 1973.
-
(1973)
New Directions in the Theory of Graphs
, pp. 239-273
-
-
Robinson, R.W.1
-
27
-
-
80053201441
-
A simple approach for finding the globally optimal Bayesian network structure
-
T. Silander and P. Myllmaki. A simple approach for finding the globally optimal Bayesian network structure. In UAI, 2006.
-
(2006)
UAI
-
-
Silander, T.1
Myllmaki, P.2
-
28
-
-
17644427718
-
Causal protein-signaling networks derived from multiparameter single-cell data
-
K. Sachs, O. Perez, D. Pe'er, D. Lauffenburger, and G. Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308, 2005.
-
Science
, vol.308
, pp. 2005
-
-
Sachs, K.1
Perez, O.2
Pe'er, D.3
Lauffenburger, D.4
Nolan, G.5
-
29
-
-
33746035971
-
The max-min hill-climbing Bayesian network structure learning algorithm
-
I. Tsamardinos, L. Brown, and C. Aliferis. The max-min hill-climbing bayesian network structure learning algorithm. Machine learning, 2006.
-
(2006)
Machine Learning
-
-
Tsamardinos, I.1
Brown, L.2
Aliferis, C.3
|