Approximating posterior distributions in belief networks using mixtures

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 416-422

  Bishop, Christopher M ^a   Lawrence, Neil ^a   Jaakkola, Tommi ^b   Jordan, Michael I ^b  

^a ASTON UNIVERSITY   (United Kingdom)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MEAN FIELD THEORY;

APPROXIMATION SCHEME; EXACT INFERENCE; LOG LIKELIHOOD; MEAN FIELD; MIXTURE COMPONENTS; POSTERIOR DISTRIBUTIONS; SIMPLE MEAN-FIELD THEORY; TRACTABLE ALGORITHMS;

MIXTURES;

EID: 84898985963     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Hinton, G. E., P. Dayan, B. J. Frey, and R. M. Neal (1995). The wake-sleep algorithm for unsupervised neural networks. Science 268, 1158-1161.
2. Jaakkola, T. (1997). Variational Methods for Inference and Estimation in Graphical Models. Ph.D. thesis, MIT.
3. Jaakkola, T. and M. I. Jordan (1997). Approximating posteriors via mixture models. To appear in Proceedings NATO ASI Learning in Graphical Models, Ed. M I. Jordan. Kluwer.
4. Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence 56, 71-113.
5. Saul, L. K., T. Jaakkola, and M. I. Jordan (1996). Mean field theory for sigmoid belief networks. Journal of Artificial Intelligence Research 4, 61-76.
6. Saul, L. K. and M. I. Jordan (1996). Exploiting tractable substructures in intractable networks. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems, Volume 8, pp. 486-492. MIT Press.