Algorithm 548: Solution of the Assignment Problem [H]
ACM Transactions on Mathematical Software (TOMS)
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Particle filters for mixture models with an unknown number of components
Statistics and Computing
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Multivariate mixtures of normals with unknown number of components
Statistics and Computing
Bayesian multivariate Poisson mixtures with an unknown number of components
Statistics and Computing
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Generic reversible jump MCMC using graphical models
Statistics and Computing
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
Statistics and Computing
Bayesian semiparametric modeling of survival data based on mixtures of B-spline distributions
Computational Statistics & Data Analysis
Block clustering with collapsed latent block models
Statistics and Computing
A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering
Expert Systems with Applications: An International Journal
Non-stationary bayesian networks based on perfect simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Improved Bayesian inference for the stochastic block model with application to large networks
Computational Statistics & Data Analysis
Univariate Bayesian nonparametric mixture modeling with unimodal kernels
Statistics and Computing
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A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture distributions with an unknown number of components is presented. The sampler is characterized by a state space consisting only of the number of components and the latent allocation variables. Its main advantage is that it can be used, with minimal changes, for mixtures of components from any parametric family, under the assumption that the component parameters can be integrated out of the model analytically. Artificial and real data sets are used to illustrate the method and mixtures of univariate and of multivariate normals are explicitly considered. The problem of label switching, when parameter inference is of interest, is addressed in a post-processing stage.