|
5667
|
Probabilistic reasoning in intelligent systems
– Judea Pearl
- 1988
|
|
877
|
A Bayesian method for the induction of probabilistic networks from data
– Gregory F. Cooper, Tom Dietterich
- 1992
|
|
451
|
Bayesian Network Classifiers
– Nir Friedman, Dan Geiger, Moises Goldszmidt
- 1997
|
|
486
|
Approximating discrete probability distributions with dependence trees
– C. I<. Chow, Sexior Member, C. N. Liu
- 1968
|
|
752
|
Learning Bayesian networks: The combination of knowledge and statistical data
– David Heckerman, David M. Chickering
- 1995
|
|
110
|
Learning Equivalence Classes Of Bayesian Network Structures
– David Maxwell Chickering
- 1996
|
|
179
|
Equivalence and synthesis of causal models
– T S Verma, J Pearl
|
|
189
|
The Bayesian Structural EM Algorithm
– Nir Friedman
- 1998
|
|
201
|
The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks
– I Beinlich, G Suermondt, R Chavez, G Cooper
- 1989
|
|
2529
|
C.J.E.: Uci repository of machine learning databases, http://www.ics.uci.edu/ ∼ mlearn/MLRepository.html
– C L Blake, Keogh
|
|
118
|
Optimal Structure Identification with Greedy Search
– David Maxwell Chickering, Craig Boutilier
- 2002
|
|
98
|
Learning Bayesian Networks is NP-Hard
– David Chickering, Dan Geiger, David Heckerman
- 1994
|
|
248
|
A Tutorial on Learning Bayesian Networks
– David Heckerman
- 1995
|
|
384
|
Causation, prediction and search
– P Spirtes, C Glymour, R Scheines
- 1993
|
|
301
|
Toward optimal feature selection
– Daphne Koller, Mehran Sahami
- 1995
|
|
55
|
Comparing Bayesian Network Classifiers
– Jie Cheng, Russell Greiner
- 1999
|
|
710
|
A tutorial on learning with Bayesian networks
– David Heckerman
- 1995
|
|
133
|
Adaptive Probabilistic Networks with Hidden Variables
– John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa, Padhraic Smyth
- 1997
|
|
175
|
A Theory Of Inferred Causation
– Judea Pearl, T.S. Verma
- 1991
|