|
5667
|
Probabilistic reasoning in intelligent systems
– Judea Pearl
- 1988
|
|
6517
|
Elements of information theory
– T Cover, J Thomas
- 1991
|
|
656
|
An introduction to variational methods for graphical models
– Michael I. Jordan
- 1999
|
|
247
|
Turbo decoding as an instance of Pearl’s belief propagation algorithm
– Robert J. Mceliece, David J. C. Mackay, Jung-fu Cheng
- 1998
|
|
213
|
The generalized distributive law
– S Aji, R Mceliece
- 2000
|
|
308
|
Generalized Belief Propagation
– Jonathan S. Yedidia, William T. Freeman, Yair Weiss
- 2000
|
|
6234
|
Maximum likelihood from incomplete data via the EM algorithm
– A. P. Dempster, N. M. Laird, D. B. Rubin
- 1977
|
|
767
|
Factor Graphs and the Sum-Product Algorithm
– Frank R. Kschischang, Brendan J. Frey, Hans-Andrea Loeliger
- 1998
|
|
3910
|
Neural Networks for Pattern Recognition
– C M Bishop
- 1995
|
|
135
|
Correctness of Local Probability Propagation in Graphical Models with Loops
– Yair Weiss
- 2000
|
|
848
|
a . The melanocyte model
– M ALA W ISTA, S
- 1971
|
|
6698
|
Statistical Learning Theory
– V N Vapnik
- 1998
|
|
214
|
Understanding belief propagation and its generalizations
– J S Yedidia, W T Freeman, Y Weiss
- 2001
|
|
426
|
D.: Markov chain Monte Carlo in practice
– W Gilks, S Richardson, Spiegelhalter
- 1996
|
|
500
|
Probabilistic Networks and Expert Systems
– R Cowell, A Dawid, S Lauritzen, D Spiegelhalter
- 1999
|
|
612
|
A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants
– Radford Neal, Geoffrey E. Hinton
- 1998
|
|
210
|
Graphical Models for Machine Learning and Digital Communication
– B Frey
- 1998
|
|
3012
|
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
– S Geman, D Geman
- 1984
|
|
315
|
Exploiting Generative Models in Discriminative Classifiers
– Tommi Jaakkola, David Haussler
- 1998
|