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Spatial Models Using Laplace Approximation Methods

  • Virgilio Gómez-Rubio
  • Roger S. Bivand
  • Håvard Rue
Reference work entry

Abstract

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.

Keywords

Posterior Distribution Gaussian Approximation Stochastic Partial Differential Equation Bayesian Hierarchical Model Laplace Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Virgilio Gómez-Rubio has been supported by the Spanish Ministry of Science and Innovation (project MTM 2008–03085) and Junta de Comunidades de Castilla–La Mancha (project PPIC11-0183-7474).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Virgilio Gómez-Rubio
    • 1
  • Roger S. Bivand
    • 2
  • Håvard Rue
    • 3
  1. 1.Department of Mathematics, School of Industrial Engineering-AlbaceteUniversity of Castilla-La ManchaAlbaceteSpain
  2. 2.Department of EconomicsNHH Norwegian School of EconomicsBergenNorway
  3. 3.Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway

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