| Sumario: | This article is aimed at reviewing a novel Bayesian approach to handle inference and
estimation in the class of generalized nonlinear models. These models include some of
the main techniques of statistical methodology, namely generalized linear models and
parametric nonlinear regression. In addition, this proposal extends to methods for the
systematic treatment of variation that is not explicitly predictedwithin themodel, through
the inclusion of random effects, and takes into account the modeling of dispersion
parameters in the class of two-parameter exponential family. The methodology is based
on the implementation of a two-stage algorithm that induces a hybrid approach based
on numerical methods for approximating the likelihood to a normal density using a
Taylor linearization around the values of current parameters in an MCMC routine.
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