A tighter bound for graphical models.
SourceNeural Computation, 13, 9, (2001), pp. 2149-2171
Article / Letter to editor
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SubjectBrain and Behaviour / Bioelectricity; Hersenen en Gedrag / Bio-elektriciteit
We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field. Additionally, we derive a third-order bound for the likelihood of sigmoid belief networks. Numerical experiments indicate that an error reduction of a factor of two is easily reached in the region where expansion-based approximations are useful.
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