Spatial Models with the Integrated Nested Laplace Approximation within Markov Chain Monte Carlo

Virgilio Gómez-Rubio, Francisco Palmí-Perales

Published 2017-02-13Version 1

The Integrated Nested Laplace Approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov Random Field (GMRF). In addition, its implementation in the R-INLA package for the R statistical software provides an easy way to fit models using INLA in practice. R-INLA implements a number of widely used latent models, including several spatial models. In addition, R-INLA can fit models in a fraction of the time than other computer intensive methods (e.g. Markov Chain Monte Carlo) take to fit the same model. Although INLA provides a fast approximation to the marginals of the model parameters, it is difficult to use it with models not implemented in R-INLA. It is also difficult to make multivariate posterior inference on the parameters of the model as INLA focuses on the posterior marginals and not the joint posterior distribution. In this paper we describe how to use INLA within the Metropolis-Hastings algorithm to fit spatial models and estimate the joint posterior distribution of a reduced number of parameters. We will illustrate the benefits of this new method with two examples on spatial econometrics and disease mapping where complex spatial models with several spatial structures need to be fitted.