Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6652
Title: Mean Field Theory for Sigmoid Belief Networks
Issue Date: 9-Oct-2013
Description: 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.
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: AIM-1570
http://hdl.handle.net/1721.1/6652
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