Mean field theory for sigmoid belief networks (1996)
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by
Lawrence K. Saul
,
Tommi Jaakkola
,
Michael I. Jordan
| Venue: | Journal of Artificial Intelligence Research |
| Citations: | 102 - 12 self |
BibTeX
@ARTICLE{Saul96meanfield,
author = {Lawrence K. Saul and Tommi Jaakkola and Michael I. Jordan},
title = {Mean field theory for sigmoid belief networks},
journal = {Journal of Artificial Intelligence Research},
year = {1996},
volume = {4},
pages = {61--76}
}
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Abstract
We develop a mean eld theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean eld theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition|the classi cation of handwritten digits. 1.
