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Using modern, fast, MCMC methods to cluster a dataset. Data are modelled as clusters of multivariate Gaussians. Each cluster can have a different covariance matrix.
Selecting the number of clusters, and covariance model, is a challenge. Often, the BIC is used as an approximation to the Bayes Factor. With MCMC, we can compute the Bayes Factor more accurately.
This methods leads to improved performance in the case of datasets with a large number of small clusters.