Bayesian finite mixtures with an unknown number of components: The allocation sampler
Statistics and Computing
Robust mixture modeling using the skew t distribution
Statistics and Computing
Bayesian multivariate Poisson mixtures with an unknown number of components
Statistics and Computing
A new graph cut-based multiple active contour algorithm without initial contours and seed points
Machine Vision and Applications
Color-texture segmentation using unsupervised graph cuts
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Statistics and Computing
Learning Gaussian mixture models with entropy-based criteria
IEEE Transactions on Neural Networks
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
Two entropy-based methods for learning unsupervised gaussian mixture models
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering
Expert Systems with Applications: An International Journal
Color image segmentation through unsupervised gaussian mixture models
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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We present full Bayesian analysis of finite mixtures of multivariate normals with unknown number of components. We adopt reversible jump Markov chain Monte Carlo and we construct, in a manner similar to that of Richardson and Green (1997), split and merge moves that produce good mixing of the Markov chains. The split moves are constructed on the space of eigenvectors and eigenvalues of the current covariance matrix so that the proposed covariance matrices are positive definite. Our proposed methodology has applications in classification and discrimination as well as heterogeneity modelling. We test our algorithm with real and simulated data.