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Global variational approximation methods in graphical models allow efficientapproximate inference of complex posterior distributions by using a simplermodel. The choice of the approximating model determines a tradeoff between thecomplexity of the approximation procedure and the quality of the approximation.In this paper, we consider variational approximations based on two classes ofmodels that are richer than standard Bayesian networks, Markov networks ormixture models. As such, these classes allow to find better tradeoffs in thespectrum of approximations. The first class of models are chain graphs, whichcapture distributions that are partially directed. The second class of modelsare directed graphs (Bayesian networks) with additional latent variables. Bothclasses allow representation of multi-variable dependencies that cannot beeasily represented within a Bayesian network.

Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables