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

  title={Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components},
  author={Panagiotis Papastamoulis},
  journal={Statistics and Computing},
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… 
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  • G. Malsiner-Walli, S. Frühwirth-Schnatter, B. Grün
  • Mathematics, Medicine
    Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
  • 2017
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