In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors.Univ Estadual Paulista, Fac Ciencia & Tecnol, Dept Estadist, Presidente Prudente, BrazilUniv Sao Paulo, Fac Med Ribeirao Preto, Dept Social Med, BR-14049 Ribeirao Preto, BrazilUniv Estadual Paulista, Fac Ciencia & Tecnol, Dept Estadist, Presidente Prudente, Brazil