Skip to content

Research at St Andrews

Spatial modeling with R-INLA: a review

Research output: Contribution to journalReview article

DOI

Open Access permissions

Open

Author(s)

Haakon Bakka, Håvard Rue, Geir-arne Fuglstad, Andrea Riebler, David Bolin, Janine Illian, Elias Krainski, Daniel Simpson, Finn Lindgren

School/Research organisations

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.
Close

Details

Original languageEnglish
Article numbere1443
Number of pages24
JournalWiley Interdisciplinary Reviews: Computational Statistics
VolumeEarly View
Early online date5 Jul 2018
DOIs
Publication statusE-pub ahead of print - 5 Jul 2018

    Research areas

  • Approximate Bayesian inference, Gaussian Markov random fields, Laplace approximations, Sparse matrices, Stochastic partial differential equations

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

    Sørbye, S., Illian, J. B., Simpson, D. P., Burlsem, D. & Rue, H., Apr 2019, In : Journal of the Royal Statistical Society: Series C (Applied Statistics). 68, 3, p. 543-564

    Research output: Contribution to journalArticle

  2. inlabru: an R package for Bayesian spatial modelling from ecological survey data

    Bachl, F. E., Lindgren, F., Borchers, D. L. & Illian, J. B., 21 Mar 2019, In : Methods in Ecology and Evolution. Early View, 7 p.

    Research output: Contribution to journalArticle

  3. Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

    Soriano-Redondo, A., Jones-Todd, C. M., Bearhop, S., Hilton, G. M., Lock, L., Stanbury, A., Votier, S. C. & Illian, J. B., 4 Mar 2019, In : Ecography. Early View

    Research output: Contribution to journalArticle

  4. Non-stationary Gaussian models with physical barriers

    Bakka, H., Vanhatalo, J., Illian, J. B., Simpson, D. & Rue, H., 18 Jan 2019, In : Spatial Statistics. In press

    Research output: Contribution to journalArticle

  5. Accounting for preferential sampling in species distribution models

    Pennino, M. G., Paradinas, I., Illian, J. B., Muñoz, F., Bellido, J. M., López-Quílez, A. & Conesa, D., 1 Jan 2019, In : Ecology and Evolution. 9, 1, p. 653-663 11 p.

    Research output: Contribution to journalArticle

ID: 254603596