-
2
-
-
0042614839
-
Object oriented Bayesian networks. A framework for topdown specification of large Bayesian networks with repetitive structures
-
Department of Computer Science, Aalborg University
-
Olav Bangsø and Pierre-Henri Wuillemin. Object oriented Bayesian networks. A framework for topdown specification of large Bayesian networks with repetitive structures. Technical report CIT-87.2-00-obphwl, Department of Computer Science, Aalborg University, 2000a.
-
(2000)
Technical Report
, vol.CIT-87.2-00-OBPHWL
-
-
Bangsø, O.1
Wuillemin, P.-H.2
-
4
-
-
0031273462
-
Adaptive probabilistic networks with hidden variables
-
John Binder, Daphne Koller, Stuart Russell, and Keiji Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning, 29(2-3):213-244, 1997.
-
(1997)
Machine Learning
, vol.29
, Issue.2-3
, pp. 213-244
-
-
Binder, J.1
Koller, D.2
Russell, S.3
Kanazawa, K.4
-
5
-
-
0030124955
-
A guide to the literature on learning probabilistic networks from data
-
Wray L. Buntine. A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8:195-210, 1996.
-
(1996)
IEEE Transactions on Knowledge and Data Engineering
, vol.8
, pp. 195-210
-
-
Buntine, W.L.1
-
6
-
-
0002607026
-
Bayesian classification (AutoClass): Theory and results
-
Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors. AAAI/MIT Press, 1996. ISBN 0-262-56097-6
-
Peter Cheeseman and John Stutz. Bayesian classification (AutoClass): Theory and results. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in knowledge discovery and data mining, pages 153-180. AAAI/MIT Press, 1996. ISBN 0-262-56097-6.
-
(1996)
Advances in Knowledge Discovery and Data Mining
, pp. 153-180
-
-
Cheeseman, P.1
Stutz, J.2
-
7
-
-
0031272327
-
Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
-
David M. Chichering and David Heckerman. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning, 29(2-3):181-212, 1997.
-
(1997)
Machine Learning
, vol.29
, Issue.2-3
, pp. 181-212
-
-
Chichering, D.M.1
Heckerman, D.2
-
9
-
-
34249832377
-
A Bayesian method for the induction of probabilistic networks from data
-
Gregory F. Cooper and Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
-
(1992)
Machine Learning
, vol.9
, pp. 309-347
-
-
Cooper, G.F.1
Herskovits, E.2
-
11
-
-
0043115674
-
Probabilistic Networks and Expert Systems
-
Springer Verlag, New York, 1999. ISBN 0-387-98767-3
-
Robert G. Cowell, A. Phillip Dawid, Steffen L. Lauritzen, and David J. Spiegelhalter. Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Sciences. Springer Verlag, New York, 1999. ISBN 0-387-98767-3.
-
(1999)
Statistics for Engineering and Information Sciences
-
-
Cowell, R.G.1
Phillip Dawid, A.2
Lauritzen, S.L.3
Spiegelhalter, D.J.4
-
12
-
-
0031269467
-
The sample complexity of learning fixed-structure Bayesian networks
-
Sanjoy Dasgupta. The sample complexity of learning fixed-structure Bayesian networks. Machine Learning, 29(2-3):165-180, 1997.
-
(1997)
Machine Learning
, vol.29
, Issue.2-3
, pp. 165-180
-
-
Dasgupta, S.1
-
13
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm
-
Arthur P. Dempster, Nan M. Laird, and Donald B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1-38, 1977.
-
(1977)
Journal of the Royal Statistical Society, Series B
, vol.39
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
15
-
-
1842641459
-
Discovering hidden variables: A structure-based approach
-
MIT Press
-
Gal Elidan, Noam Lotner, Nir Friedman, and Daphne Koller. Discovering hidden variables: A structure-based approach. In Advances in Neural Information Processing Systems 13, pages 479-485. MIT Press, 2000.
-
(2000)
Advances in Neural Information Processing Systems
, vol.13
, pp. 479-485
-
-
Elidan, G.1
Lotner, N.2
Friedman, N.3
Koller, D.4
-
17
-
-
0031276011
-
Bayesian network classifiers
-
Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine Learning, 29(2-3):131-163, 1997a.
-
(1997)
Machine Learning
, vol.29
, Issue.2-3
, pp. 131-163
-
-
Friedman, N.1
Geiger, D.2
Goldszmidt, M.3
-
18
-
-
84880688943
-
Learning probabilistic relational models
-
Morgan Kaufmann Publishers
-
Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer. Learning probabilistic relational models. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300-1309. Morgan Kaufmann Publishers, 1999.
-
(1999)
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
, pp. 1300-1309
-
-
Friedman, N.1
Getoor, L.2
Koller, D.3
Pfeffer, A.4
-
21
-
-
0037262841
-
Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks
-
Nir Friedman and Daphne Koller. Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1-2):99-125, 2003.
-
(2003)
Machine Learning
, vol.50
, Issue.1-2
, pp. 99-125
-
-
Friedman, N.1
Koller, D.2
-
25
-
-
0041779094
-
Learning probabilistic relational models
-
Saso Dzeroski and Nada Lavrac, editors. Springer Verlag, Berlin, Germany. ISBN 3-540-42289-7. See also (Friedman et al., 1999)
-
Lise Getoor, Nir Friedman, Daphne Koller, and Avi Pfeffer. Learning probabilistic relational models. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 307-338. Springer Verlag, Berlin, Germany, 2001. ISBN 3-540-42289-7. See also (Friedman et al., 1999).
-
(2001)
Relational Data Mining
, pp. 307-338
-
-
Getoor, L.1
Friedman, N.2
Koller, D.3
Pfeffer, A.4
-
26
-
-
0000357775
-
On use of the EM algorithm for penalized likelihood estimation
-
Peter J. Green. On use of the EM algorithm for penalized likelihood estimation. Journal of the Royal Statistical Society, Series B, 52(3):443-452, 1990.
-
(1990)
Journal of the Royal Statistical Society, Series B
, vol.52
, Issue.3
, pp. 443-452
-
-
Green, P.J.1
-
28
-
-
0041486577
-
A Bayesian approach to learning causal networks
-
Microsoft Research
-
David Heckerman. A Bayesian approach to learning causal networks. Technical Report MSR-TR-95-04, Microsoft Research, 1995a.
-
(1995)
Technical Report
, vol.MSR-TR-95-04
-
-
Heckerman, D.1
-
29
-
-
0003846041
-
A tutorial on learning with Bayesian networks
-
Microsoft Research
-
David Heckerman. A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, 1995b.
-
(1995)
Technical Report
, vol.MSR-TR-95-06
-
-
Heckerman, D.1
-
30
-
-
34249761849
-
Learning Bayesian networks: The combination of knowledge and statistical data
-
David Heckerman, Dan Geiger, and David 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, D.M.3
-
31
-
-
0030343462
-
Distinguishing "Missing at Random" and "Missing Completely At Random"
-
Daniel F. Heitjan and Srabashi Basu. Distinguishing "Missing At Random" and "Missing Completely At Random". The American Statistician, 50(3):207-213, 1996.
-
(1996)
The American Statistician
, vol.50
, Issue.3
, pp. 207-213
-
-
Heitjan, D.F.1
Basu, S.2
-
36
-
-
0032273105
-
Learning probabilistic networks
-
Paul J. Krause. Learning probabilistic networks. The Knowledge Engineering Review, 13(4):321-351, 1998.
-
(1998)
The Knowledge Engineering Review
, vol.13
, Issue.4
, pp. 321-351
-
-
Krause, P.J.1
-
38
-
-
0028482006
-
Learning Bayesian belief networks: An approach based on the MDL principle
-
Wai Lam and Fahiem Bacchus. Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10(4):269-293, 1994.
-
(1994)
Computational Intelligence
, vol.10
, Issue.4
, pp. 269-293
-
-
Lam, W.1
Bacchus, F.2
-
43
-
-
0003984994
-
-
Marko Publishing ApS, Aalborg, Denmark. ISBN 8-777-51150-6
-
Lars Mathiasen, Andreas Munk-Nielsen, Peter A. Nielsen, and Jan Stage. Object-oriented analysis & design. Marko Publishing ApS, Aalborg, Denmark, 2000. ISBN 8-777-51150-6.
-
(2000)
Object-oriented Analysis & Design
-
-
Mathiasen, L.1
Munk-Nielsen, A.2
Nielsen, P.A.3
Stage, J.4
-
47
-
-
0013227369
-
Knowledge engineering for large belief networks
-
Morgan Kaufmann Publishers
-
Malcolm Pradhan, Gregory Provan, Blackford Middleton, and Max Henrion. Knowledge engineering for large belief networks. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 484-490. Morgan Kaufmann Publishers, 1994.
-
(1994)
Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence
, pp. 484-490
-
-
Pradhan, M.1
Provan, G.2
Middleton, B.3
Henrion, M.4
-
48
-
-
84953405534
-
-
Cambridge University Press, Cambridge, UK. ISBN 0-521-46086-7
-
Brian D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK, 1996. ISBN 0-521-46086-7.
-
(1996)
Pattern Recognition and Neural Networks
-
-
Ripley, B.D.1
-
50
-
-
0001457227
-
Counting labeled acyclic digraphs
-
Frank Harary, editor. Academic Press, New York
-
Robert W. Robinson. Counting labeled acyclic digraphs. In Frank Harary, editor, New directions in the theory of graphs, pages 239-273. Academic Press, New York, 1973.
-
(1973)
New Directions in the Theory of Graphs
, pp. 239-273
-
-
Robinson, R.W.1
-
51
-
-
0000120766
-
Estimating the dimension of a model
-
Gideon Schwarz. Estimating the dimension of a model. Annals of Statistics, 6:461-464, 1978.
-
(1978)
Annals of Statistics
, vol.6
, pp. 461-464
-
-
Schwarz, G.1
-
52
-
-
0003260456
-
Density estimation for statistics and data analysis
-
Chapman and Hall, London, UK. ISBN 0-412-24620-1
-
Bernard W. Silverman. Density Estimation for Statistics and Data Analysis. Monographs on statistics and applied probability. Chapman and Hall, London, UK, 1986. ISBN 0-412-24620-1.
-
(1986)
Monographs on Statistics and Applied Probability
-
-
Silverman, B.W.1
-
53
-
-
0003614273
-
-
Springer Verlag, New York. ISBN 0-387-97979-4
-
Peter Spirtes, Clark Glymour, and Richard Scheines. Causation, Prediction, and Search. Springer Verlag, New York, 1993. ISBN 0-387-97979-4.
-
(1993)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
59
-
-
0037810162
-
Inference in multiply sectioned bayesian networks with extended Shafer-Shenoy and lazy propagation
-
Morgan Kaufmann Publishers
-
Yang Xiang and Finn V. Jensen. Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 680-687. Morgan Kaufmann Publishers, 1999.
-
(1999)
Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
, pp. 680-687
-
-
Xiang, Y.1
Jensen, F.V.2
-
60
-
-
84990616603
-
Multiply sectioned Bayesian networks and junction forests for large knowledge-based systems
-
Yang Xiang, David Poole, and Michael P. Beddoes. Multiply sectioned Bayesian networks and junction forests for large knowledge-based systems. Computational Intelligence, 9(2): 171-220, 1993.
-
(1993)
Computational Intelligence
, vol.9
, Issue.2
, pp. 171-220
-
-
Xiang, Y.1
Poole, D.2
Beddoes, M.P.3
|