  The integrated nested Laplace approximation (INLA) for Bayesian inference is
an efficient approach to estimate the posterior marginal distributions of the
parameters and latent effects of Bayesian hierarchical models that can be
expressed as latent Gaussian Markov random fields (GMRF). The representation as
a GMRF allows the associated software R-INLA to estimate the posterior
marginals in a fraction of the time as typical Markov chain Monte Carlo
algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to
increase the number of models that it can fit to conditional latent GMRF. In
this paper we review the use of BMA with INLA and propose a new example on
spatial econometrics models.
