Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components

@article{Papastamoulis2020ClusteringMD,
  title={Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components},
  author={Panagiotis Papastamoulis},
  journal={Statistics and Computing},
  year={2020},
  volume={30},
  pages={485-506}
}
Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for clustering multivariate data using a latent Gaussian model which resembles the factor analysis model. The flexibility provided by overfitting mixture models yields a simple and efficient way in order to estimate the unknown number of clusters and model parameters by Markov chain Monte Carlo sampling. The present study extends this approach by considering a set of eight parameterizations, giving rise to… Expand
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