Mean Field Theory for Sigmoid Belief Networks
Author(s)
Saul, Lawrence K.; Jaakkola, Tommi; Jordan, Michael I.
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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.
Date issued
1996-08-01Other identifiers
AIM-1570
Series/Report no.
AIM-1570