Model-independent mean-field theory as a local method for approximate propagation of information

Network: Computation in Neural Systems

Volumn 10, Issue 1, 1999, Pages 93-105

  Haft, M ^a   Hofmann, R ^a   Tresp, V ^a  

^a SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHILD; DECISION SUPPORT SYSTEM; DIFFERENTIAL DIAGNOSIS; HUMAN; INFORMATION SCIENCE; PROBABILITY;

CHILD; DECISION SUPPORT TECHNIQUES; DIAGNOSIS, DIFFERENTIAL; HUMANS; INFORMATION THEORY; NEURAL NETWORKS (COMPUTER); PROBABILITY;

EID: 0042138305     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_10_1_006     Document Type: Article

References (26)

1. Pearl J 1988 Probabilistic Reasoning in Intelligent Systems 2nd edn (San Mateo, CA: Morgan Kaufmann)
2. Cooper G 1990 Computational complexity of probabilistic inference using Bayesian belief networks Artificial Intell. 42 393-405
3. Binney J J, Dowrick N J, Fisher A J and Newman M E J 1995 The Theory of Critical Phenomena 3rd edn (Oxford: Oxford University Press)
4. Parisi G 1988 Statistical Field Theory (Reading, MA: Addison-Wesley)
5. Peterson C and Anderson J R 1987 A mean field theory learning algorithm for neural networks Complex Systems 1 995-1019
6. Peterson C and Hartman E 1989 Explorations of the mean field theory learning algorithm Neural Networks 2 475-94
7. Hofmann T and Buhmann J 1997 Pairwise data clustering by deterministic annealing IEEE Trans. Patt. Anal. Machine Intell. 19 1-14
8. Haft M 1998 Robust 'topological' codes by keeping control of internal redundancy Phys. Rev. Lett. 81 4016-9
9. Lauritzen S L and Spiegelhalter D J 1988 Local computations with probabilities on graphical structures and their application to expert systems J. R. Stat. Soc. B 50 154-227
10. Jensen F V, Lauritzen S L and Olsen K G 1990 Bayesian updating in causal probabilistic networks by local computations Comput. Stat. Q. 4 269-82
11. Whittaker J 1990 Graphical Models in Applied Multivariate Statistics (New York: Wiley)
12. Saul L K and Jordan M I 1995 Exploiting tractable substructures in untractable networks Advances in Neural Information Processing Systems (Proc. 1995 Conf.) ed D Touretzky, M Moser and M Hasselmo (Cambridge, MA: MIT Press) pp 486-92
13. Saul L K, Jaakkola T and Jordan M I 1996 Mean field theory for sigmoid belief networks J. Artificial Intell. Res. 461-76
14. Haft M, Hofmann R and Tresp V 1997 Model-independent mean field theory as a local method for approximate propagation of information Technical Report http://www7.informatik.tumuenchen.de/hofmannr/mf_abstr.html
15. Bishop C M, Lawrence N D, Jaakkola T and Jordan M I 1998 Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems 10 (Proc. 1997 Conf.) ed M I Jordan, M J Kearns and S A Solla (Cambridge, MA: MIT Press) pp 416-22
16. Lawrence N D, Bishop C M and Jordan M I 1998 Mixture representations for inference and learning in Boltzmann machines Uncertainty in Artificial Intelligence (Proc. 14th Conf.) (San Mateo, CA: Morgan Kaufmann) pp 320-7
17. Jaakkola T S and Jordan M I 1998 Improving the mean field approximation via the use of mixture distributions Learning in Graphical Models ed M I Jordan (Dordrecht: Kluwer) pp 163-73
18. Cover T M and Thomas J A 1991 Elements of Information Theory (New York: Wiley)
19. Serot B D and Walecka J D 1986 The Relativistic Nuclear Many Body Problem (Advances in Nuclear Physics 16) (New York: Plenum)
20. Glendenning N K, von Eiff D, Haft M, Lenske H and Weigel M K 1993 Relativistic mean field calculations of Λ-and Σ-hypernuclei Phys. Rev. C 48 889-95
21. Fischer K H and Hertz J A 1991 Spin Glasses (Cambridge: Cambridge University Press)
22. van Hemmen J L and Kühn R 1986 Nonlinear neural networks Phys. Rev. Lett. 57 913-6
23. van Hemmen J L and Kühn R 1991 Collective phenomena in neural networks Models of Neural Networks I ed E Domany, J L van Hemmen and K Schulten (Berlin: Springer) pp 1-113
24. Hertz J A, Krogh A and Palmer R G 1991 Introduction to the Theory of Neural Computation (Reading, MA: Addison-Wesley)
25. Jensen V J 1996 An Introduction to Bayesian Networks (London: UCL Press)
26. Frey B J and Hinton G E 1999 Variational learning in non-linear Gaussian belief networks Neural Comput. 11 193-213 (available at http://www.cs.utoronto.ca/frey)