Approximating posterior distributions in belief networks using mixtures

An Introduction to Variational Methods for Graphical Models

Machine Learning

Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution

Neural Processing Letters

Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm

EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

Split variational inference

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning

Latent tree models and approximate inference in Bayesian networks

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2

Latent tree models and approximate inference in Bayesian networks

Journal of Artificial Intelligence Research

Mean-field methods for a special class of belief networks

Journal of Artificial Intelligence Research

Variational inference for grammar induction with prior knowledge

ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

Mixture approximations to Bayesian networks

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Mixture representations for inference and learning in Boltzmann machines

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Mixtures of kikuchi approximations

ECML'06 Proceedings of the 17th European conference on Machine Learning

Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review

Some aspects of latent structure analysis

SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection