A Bayesian model selection method with applications
Computational Statistics & Data Analysis
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Bayesian analysis of finite mixture models of distributions from exponential families
Computational Statistics
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Reversible Jump MCMC in mixtures of normal distributions with the same component means
Computational Statistics & Data Analysis
Bayesian model choice based on Monte Carlo estimates of posterior model probabilities
Computational Statistics & Data Analysis
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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Two new approaches to estimate Bayes factors in a finite mixture model context are proposed. Specifically, two algorithms to estimate them and their errors are derived by decomposing the resulting marginal densities. Then, through Bayes factor comparisons, the appropriate number of components for the mixture model is obtained. The approaches are based on simple theory (Monte Carlo methods and cluster sampling), what makes them appealing tools in this context. The performance of both algorithms is studied for different situations and the procedures are illustrated with some previously published data sets.