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Mixture models provide a flexible representation of heterogeneity in a finitenumber of latent classes. From the Bayesian point of view, Markov Chain MonteCarlo methods provide a way to draw inferences from these models. Inparticular, when the number of subpopulations is considered unknown, moresophisticated methods are required to perform Bayesian analysis. The ReversibleJump Markov Chain Monte Carlo is an alternative method for computing theposterior distribution by simulation in this case. Some problems associatedwith the Bayesian analysis of these class of models are frequent, such as theso-called "label-switching" problem. However, as the level of heterogeneity inthe population increases, these problems are expected to become less frequentand the model's performance to improve. Thus, the aim of this work is toevaluate the normal mixture model fit using simulated data under differentsettings of heterogeneity and prior information about the mixture proportions.A simulation study is also presented to evaluate the model's performanceconsidering the number of components known and estimating it. Finally, themodel is applied to a censored real dataset containing antibody levels ofCytomegalovirus in individuals.

Mixture models applied to heterogeneous populations