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In this paper, a scale mixture of Normal distributions model is developed forclassification and clustering of data having outliers and missing values. Theclassification method, based on a mixture model, focuses on the introduction oflatent variables that gives us the possibility to handle sensitivity of modelto outliers and to allow a less restrictive modelling of missing data.Inference is processed through a Variational Bayesian Approximation and aBayesian treatment is adopted for model learning, supervised classification andclustering.

Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data