In the current dissertation, we study the mixture regression models and present two Bayesian
methodologies for their estimation. The first one considers the number of components is known
and we propose the use of two Bayesian model selection criteria, DIC and EBIC, to identify
the number of components. In the other one, we propose a reversible jump algorithm with splitmerge steps that estimates parameters and the number of components. We apply the proposed
methodologies and also the EM algorithm, already available in R package, for simulated dataset and for Brazilian educational data, studying the relationship among the Basic Education
Development Index and some socioeconomic and demographic data.