  In this paper, we discuss computational aspects to obtain accurate inferences
for the parameters of the generalized gamma (GG) distribution. Usually, the
solution of the maximum likelihood estimators (MLE) for the GG distribution
have no stable behavior depending on large sample sizes and good initial values
to be used in the iterative numerical algorithms. From a Bayesian approach,
this problem remains, but now related to the choice of prior distributions for
the parameters of this model. We presented some exploratory techniques to
obtain good initial values to be used in the iterative procedures and also to
elicited appropriate informative priors. Finally, our proposed methodology is
also considered for data sets in the presence of censorship.
