Bibliography

Alon MegiddoAlon Megiddo1990

Alon, N. Megiddo, N. 1990.
Parallel linear programming in fixed dimension almost surely in constant time
In IEEE Symposium on Foundations of Computer Science, 574-582.

Beinlich, Suermondt, Chavez, CooperBeinlich et al.1989

Beinlich, I., Suermondt, G., Chavez, R., Cooper, G. 1989.
The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks
In Proceedings of the 2nd European Conference on AI and Medicine. Springer-Verlag, Berlin.

HintonHinton1999

Hinton, G. 1999.
Products of experts
In Proceedings of the Ninth International Conference on Artificial Neural Networks,  1, 1-6.

Jaakkola JordanJaakkola Jordan1999

Jaakkola, T. Jordan, M. 1999.
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research, 10, 291-322.

Kappen WiegerinckKappen Wiegerinck2002

Kappen, H. Wiegerinck, W. 2002.
A novel iteration scheme for the cluster variation method
In Advances in Neural Information Processing Systems,  14, In press.

KhachiyanKhachiyan1979

Khachiyan, L. 1979.
A polynomial algorithm in linear programming (in Russian)
Doklady Akademiia Nauk SSSR, 224, 1093-1096.
English translation: Soviet Mathematics Doklady, Volume 20, 191-194.

Leisink KappenLeisink Kappen2001

Leisink, M. Kappen, H. 2001.
A tighter bound for graphical models
Neural Computation, 13(9), 2149-2171.

Leisink KappenLeisink Kappen2002

Leisink, M. Kappen, H. 2002.
Means, correlations and bounds
In Dietterich, T. G., Becker, S., Ghahramani, Z., Advances in Neural Information Processing Systems,  14, 455-462, Cambridge, MA. MIT Press.

Murphy, Weiss, JordanMurphy et al.1999

Murphy, K., Weiss, Y., Jordan, M. 1999.
Loopy belief propagation for approximate inference: An empirical study
In Uncertainty in Artificial Intelligence,  15, 467-475.

NealNeal1992

Neal, R. 1992.
Connectionist learning of belief networks
Artificial intelligence, 56, 71-113.

PearlPearl1988

Pearl, J. 1988.
Probabilistic reasoning in intelligent systems, 204-210.
Morgan Kaufmann Publishers, Inc.
ISBN 0-934613-73-7.

Peterson AndersonPeterson Anderson1987

Peterson, C. Anderson, J. 1987.
A mean field theory learning algorithm for neural networks
Complex systems, 1, 995-1019.

PlefkaPlefka1981

Plefka, T. 1981.
Convergence condition of the TAP equation for the infinite-ranged ising spin glass model
Journal of Physics A: Mathematical and general, 15, 1971-1978.

Saul, Jaakkola, JordanSaul et al.1996

Saul, L., Jaakkola, T., Jordan, M. 1996.
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research, 4, 61-76.

Shwe, Middleton, Heckerman, Henrion, Horvitz, Lehmann, CooperShwe et al.1991

Shwe, M., Middleton, B., Heckerman, D., Henrion, M., Horvitz, E., Lehmann, H., Cooper, G. 1991.
Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base
Methods of Information in Medicine, 30, 241-255.

Thouless, Andersson, PalmerThouless et al.1977

Thouless, D., Andersson, P., Palmer, R. 1977.
Solution of `solvable model of a spin glass'
Philisophical Magazine, 35(3), 593-601.

ToddTodd2002

Todd, M. 2002.
The many facets of linear programming
Mathematical Programming, 91, 417-436.

Wainwright, Jaakkola, WillskyWainwright et al.2002

Wainwright, M., Jaakkola, T., Willsky, A. 2002.
A new class of upper bounds on the log partition function
In Uncertainty in Artificial Intelligence,  18.

Yedidia, Freeman, WeissYedidia et al.2000

Yedidia, J., Freeman, W., Weiss, Y. 2000.
Generalized belief propagation
In Advances in Neural Information Processing Systems,  13, 689-695.

Zhang PooleZhang Poole1994

Zhang, N. Poole, D. 1994.
A simple approach to Bayesian network computations
In Proceedings of the Tenth Canadian Conference on Artificial Intelligence, 171-178.