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Unsupervised and Semi-supervised Dirichlet Process Heterogeneous Mixtures

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Implements Dirichlet Process Heterogeneous Mixtures of exponential family distributions for clustering heterogeneous data without choosing the number of clusters. Inference can be performed with Gibbs sampling or coordinate ascent mean-field variational inference. For semi-supervised learning, Gibbs sampling supports must-link and cannot-link constraints. A novel variational inference algorithm was derived to handle must-link constraints.

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