The parameters of the beta mixed effects model can be estimated from the Bayesian approach. Bayesian inference on mixed beta models is not straightforward because the posterior distribution is not available analytically. The Markov Chain Monte Carlo (MCMC) technique is the standard approach for fitting these models (citep{zuniga:2013}). many problems in terms of convergence and calculation time. Additionally, the implementation itself can be problematic, especially for end users who may not be programming experts. There are several software platforms for fitting generic random effects models via MCMC, including JAGS citedp{Plummer03jags:a}, BayesX citedp{BayesX}, and WinBUGS citedp{Lunn2000}, among others. The INLA (Integrated Nested Laplace Approximation) approach is a new tool for Bayesian inference on latent Gaussian models when the emphasis is on posterior marginal distributions citedp{ Rue2009}. INLA replaces MCMC simulations with precise and deterministic approximations of posterior marginal distributions. A computer implementation...