Spatial Models Using Laplace Approximation Methods

Virgilio Gómez-Rubio, Roger S. Bivand, Haavard Rue

Research output: Chapter in Book/Report/Conference proceedingChapter


Bayesian inference has been at the center of the development of spatial statistics in recent years. In particular, Bayesian hierarchical models including several fixed and random effects have become very popular in many different fields. Given that inference on these models is seldom available in closed form, model fitting is usually based on simulation methods such as Markov chain Monte Carlo. However, these methods are often very computationally expensive and a number of approximations have been developed. The integrated nested Laplace approximation (INLA) provides a general approach to computing the posterior marginals of the parameters in the model. INLA focuses on latent Gaussian models, but this is a class of methods wide enough to tackle a large number of problems in spatial statistics. In this chapter, we describe the main advantages of the integrated nested Laplace approximation. Applications to many different problems in spatial statistics will be discussed as well.
Original languageEnglish (US)
Title of host publicationHandbook of Regional Science
PublisherSpringer Berlin Heidelberg
Number of pages16
ISBN (Print)9783642362033
StatePublished - May 16 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: V. Gómez-Rubio has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (JCCM, Spain) and Fondo Europeo de Desarrollo Regional, and grants MTM2008-03085 and MTM2016-77501-P, funded by the Ministerio de Economía y Competitividad (Spain).


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