Home > Research > Publications & Outputs > INLA or MCMC?
View graph of relations

INLA or MCMC?: a tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
<mark>Journal publication date</mark>2014
<mark>Journal</mark>Journal of Statistical Computation and Simulation
Issue number10
Volume84
Number of pages19
Pages (from-to)2266-2284
Publication StatusPublished
Early online date18/04/13
<mark>Original language</mark>English

Abstract

We investigate two options for performing Bayesian inference on spatial log-Gaussian Cox processes assuming a spatially continuous latent field: Markov chain Monte Carlo (MCMC) and the integrated nested Laplace approximation (INLA). We first describe the device of approximating a spatially continuous Gaussian field by a Gaussian Markov random field on a discrete lattice, and present a simulation study showing that, with careful choice of parameter values, small neighbourhood sizes can give excellent
approximations. We then introduce the spatial log-Gaussian Cox process and describe MCMC and INLA methods for spatial prediction within this model class. We report the results of a simulation study in which we compare the Metropolis-adjusted Langevin Algorithm (MALA) and the technique of approximating the continuous latent field by a discrete one, followed by approximate Bayesian inference via INLA over a selection of 18 simulated scenarios. The results question the notion that the latter technique is both
significantly faster and more robust than MCMC in this setting; 100,000 iterations of the MALA algorithm running in 20 min on a desktop PC delivered greater predictive accuracy than the default INLA strategy,
which ran in 4 min and gave comparative performance to the full Laplace approximation which ran in 39 min.