Improving the Mean Field Approximation Via the Use of Mixture Distributions-Reference-Cited by-同舟云学术

Improving the Mean Field Approximation Via the Use of Mixture Distributions

Published:1998 Issue: Volume: Page:163-173
ISSN:
Container-title:Learning in Graphical Models
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Author:

Jaakkola Tommi S.,Jordan Michael I.

Publisher

Springer Netherlands

Link

http://link.springer.com/content/pdf/10.1007/978-94-011-5014-9_6

Reference8 articles.

1. Bishop, C., Lawrence, N., Jaakkola, T. S., & Jordan, M. I. Approximating posterior distributions in belief networks using mixtures. In M. I. Jordan, M. J. Kearns, & S. A. Solla, Advances in Neural Information Processing Systems 10, MIT Press, Cambridge MA (1998).

2. Ghahramani, Z. & Jordan, M. I. Factorial Hidden Markov models. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8, MIT Press, Cambridge MA (1996).

3. Neal, R. Connectionist learning of belief networks, Artificial Intelligence, 56: 71–113 (1992).

4. Parisi, G. Statistical Field Theory. Addison-Wesley: Redwood City (1988).

5. Peterson, C. & Anderson, J. R. A mean field theory learning algorithm for neural networks. Complex Systems1: 995–1019 (1987).

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