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Bayesian Spatial Modelling with R-INLA
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DOI：10.18637/jss.v063.i19.png)
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Abstract
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The principles behind the interface to continuous domain spatial models in the R INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized) linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrom 2011), one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial models, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.
Keywords:
Bayesian inference
Gaussian Markov random fields
stochastic partial differential equations
Laplace approximation
R
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8.1
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622
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4.6W
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