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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

October 2006, Vol. 18, No. 10, Pages 2283-2292
(doi: 10.1162/neco.2006.18.10.2283)
© 2006 Massachusetts Institute of Technology
Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines
Article PDF (79.95 KB)
Abstract

A Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple yet statistically consistent.