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Divergence measures and latent variable models

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Alpha-divergences have attracted recent attention as a measure of divergence for generalizing algorithms for approximate inference (like Variational Bayes and Expectation Propagation) into a unified framework. This talk explores the use and effect of such divergence measures under mutivariate mixtures of Gaussians, an example of a simple non-trivial latent variable model. In particular, symmetry-breaking, Ockam-hills for different measures of divergence, behaviour under multiple modes, and practical results are discussed.

This talk is part of the Inference Group series.

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