|
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
|
|
469
|
Learning in graphical models
– Michael I. Jordan
- 2004
|
|
208
|
A Unifying Review of Linear Gaussian Models
– Sam Roweis, Zoubin Ghahramani
- 1999
|
|
423
|
Bayesian Networks and Decision Graphs
– F V Jensen
- 2001
|
|
214
|
Operations for Learning with Graphical Models
– Wray L. Buntine
- 1994
|
|
156
|
A Guide to the Literature on Learning Probabilistic Networks From Data
– Wray Buntine
- 1996
|
|
38
|
Improving Markov chain Monte Carlo model search for data mining. Machine learning 50
– P Giudici, R Castelo
- 2003
|
|
189
|
The Bayesian Structural EM Algorithm
– Nir Friedman
- 1998
|
|
7
|
Fusion and propogation with multiple observations in belief networks
– M Peot, R Shachter
- 1991
|
|
29
|
E cient approximations for the marginal likelihood of Bayesian networks with hidden variables
– D Chickering, D Heckerman
- 1997
|
|
20
|
Learning Bayesian networks in the presence of missing values and hidden variables
– N Friedman
- 1997
|
|
500
|
Probabilistic Networks and Expert Systems
– R Cowell, A Dawid, S Lauritzen, D Spiegelhalter
- 1999
|
|
119
|
Inference in belief networks: A procedural guide
– Cecil Huang, Adnan Darwiche
- 1996
|
|
426
|
D.: Markov chain Monte Carlo in practice
– W Gilks, S Richardson, Spiegelhalter
- 1996
|
|
213
|
The generalized distributive law
– S Aji, R Mceliece
- 2000
|
|
240
|
Bayesian updating in causal probabilistic networks by local computations
– Finn V Jensen, Steffen L Lauritzen, Kristian G Olesen
- 1990
|
|
247
|
Turbo decoding as an instance of Pearl’s belief propagation algorithm
– Robert J. Mceliece, David J. C. Mackay, Jung-fu Cheng
- 1998
|
|
766
|
Causality: Models, Reasoning, and Inference
– J Pearl
|