Mean Field Theory for Sigmoid Belief Networks
<|reference_start|>Mean Field Theory for Sigmoid Belief Networks: 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.<|reference_end|>
@article{saul1996mean,
    title={Mean Field Theory for Sigmoid Belief Networks},
    author={L. K. Saul, T. Jaakkola, M. I. Jordan},
    journal={Journal of Artificial Intelligence Research, Vol 4, (1996), 61-76},
    year={1996},
    archivePrefix={arXiv},
    eprint={cs/9603102},
    primaryClass={cs.AI}
}