Mean Field Theory for Sigmoid Belief Networks
Artificial Intelligence
2009-09-25 v1
Abstract
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field 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 classification of handwritten digits.
Keywords
Cite
@article{arxiv.cs/9603102,
title = {Mean Field Theory for Sigmoid Belief Networks},
author = {L. K. Saul and T. Jaakkola and M. I. Jordan},
journal= {arXiv preprint arXiv:cs/9603102},
year = {2009}
}
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