Efficient learning in Boltzmann machines using linear response theory
Neural Computation
Approximating posterior distributions in belief networks using mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to variational methods for graphical models
Learning in graphical models
Computing upper and lower bounds on likelihoods in intractable networks
Computing upper and lower bounds on likelihoods in intractable networks
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Some aspects of latent structure analysis
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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We develop a new extension to the Mean-Field approximation for inference in graphical models which has advantages over other approximation schemes which have been proposed. The method is economical in its use of variational parameters and the approximating conditional distribution can be specified with direct reference to the dependence structure of the variables in the graphical model. We apply the method to sigmoid belief networks.