TY - CHAP

T1 - Spatial Models Using Laplace Approximation Methods

AU - Gómez-Rubio, Virgilio

AU - Bivand, Roger S.

AU - Rue, Haavard

N1 - 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).

PY - 2019/5/16

Y1 - 2019/5/16

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/660116

UR - http://link.springer.com/10.1007/978-3-642-36203-3_104-1

U2 - 10.1007/978-3-642-36203-3_104-1

DO - 10.1007/978-3-642-36203-3_104-1

M3 - Chapter

SN - 9783642362033

SP - 1

EP - 16

BT - Handbook of Regional Science

PB - Springer Berlin Heidelberg

ER -