Efficient learning in Boltzmann machines using linear response theory
Neural Computation
Variational learning in nonlinear Gaussian belief networks
Neural Computation
An introduction to variational methods for graphical models
Learning in graphical models
Bayesian parameter estimation via variational methods
Statistics and Computing
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Upper bound for variational free energy of Bayesian networks
Machine Learning
Computational Statistics & Data Analysis
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The consistency problem of both mean field and variational Bayes estimators in the context of linear state space models is investigated. We prove that the mean field approximation is asymptotically consistent when the variances of the noise variables in the system are sufficiently small, but neither the mean field estimator nor the variational Bayes estimator is always asymptotically consistent as the 'sample size' becomes large. The 'gap' between the estimators and the true values is roughly estimated.