Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Factor models for multivariate count data
Journal of Multivariate Analysis
Multivariate Poisson regression with covariance structure
Statistics and Computing
Multivariate mixtures of normals with unknown number of components
Statistics and Computing
Bayesian finite mixtures with an unknown number of components: The allocation sampler
Statistics and Computing
Probabilistic self-organizing maps for qualitative data
Neural Networks
A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering
Expert Systems with Applications: An International Journal
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In this paper we present Bayesian analysis of finite mixtures of multivariate Poisson distributions with an unknown number of components. The multivariate Poisson distribution can be regarded as the discrete counterpart of the multivariate normal distribution, which is suitable for modelling multivariate count data. Mixtures of multivariate Poisson distributions allow for overdispersion and for negative correlations between variables. To perform Bayesian analysis of these models we adopt a reversible jump Markov chain Monte Carlo (MCMC) algorithm with birth and death moves for updating the number of components. We present results obtained from applying our modelling approach to simulated and real data. Furthermore, we apply our approach to a problem in multivariate disease mapping, namely joint modelling of diseases with correlated counts.