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In many real world scenarios, mixture models have successfully been used for analyzing features in data ([11, 13, 21]). Usually, multivariate Gaussian distributions for continuous data ([2, 8, 4]) or Bayesian networks for nominal data ([15, 16]) are applied. In this paper, we combine both approaches in a family of Bayesian models for continuous data that(More)
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