Spatial modeling with R-INLA: A review

Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J. , Krainski, E., Simpson, D. and Lindgren, F. (2018) Spatial modeling with R-INLA: A review. Wiley Interdisciplinary Reviews: Computational Statistics, 10(6), e1443. (doi:10.1002/wics.1443)

Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J. , Krainski, E., Simpson, D. and Lindgren, F. (2018) Spatial modeling with R-INLA: A review. Wiley Interdisciplinary Reviews: Computational Statistics, 10(6), e1443. (doi:10.1002/wics.1443)

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Abstract

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R‐INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R‐INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Matérn Gaussian random fields. In this review, we discuss the large success of spatial modeling with R‐INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight‐forward approaches for nonstationary spatial models and nonseparable space–time models.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Illian, Professor Janine
Authors: Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J., Krainski, E., Simpson, D., and Lindgren, F.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Wiley Interdisciplinary Reviews: Computational Statistics
ISSN:1939-5108
ISSN (Online):1939-0068
Published Online:05 July 2018
Copyright Holders:Copyright © 2018 Wiley Periodicals, Inc.
First Published:First published in Wiley Interdisciplinary Reviews: Computational Statistics 10(6):e1443
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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