# 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…

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