In this paper, we present a Bayesian analysis for extra-Binomial variability models introduced in the literature. We also consider the use of mixture distributions to model extra-Binomial variability and the introduction of covariates. Considering Gibbs Sampling with Metropolis-Hastings algorithms, we obtain Monte Carlo estimates for the posterior quantities of interest. The methodology is illustrated in two real data sets.