Classification of multivariate data using Dirichlet process mixture models

We address the problem of multivariate data classification by the nonparametric Bayesian methodology, where the priors are modeled as Dirichlet processes. Sets of series of multivariate data are observed, where each vector in the series is modeled by a Gaussian linear regression. Each class is defined by the unknown matrices of linear coefficients of the model and the covariance matrices of the errors of the model. The number of different classes is unknown. For the unknown coefficients and covariance matrices we adopt a conjugate prior, the matrix-normal - inverse Wishart distribution. We implement the classification by Markov chain Monte Carlo sampling. The proposed approach is demonstrated by extensive computer simulations.