References

Blangiardo, Marta, and Michela Cameletti. 2015. Spatial and Spatio-Temporal Bayesian Models with r-INLA. John Wiley & Sons, Ltd. https://doi.org/https://doi.org/10.1002/9781118950203.
Diggle, Peter J., Paula Moraga, Barry Rowlingson, and Benjamin M. Taylor. 2013. “Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm.” Statistical Science 28 (4): 542–63. https://doi.org/10.1214/13-STS441.
Lindgren, Finn, and Håvard Rue. 2015. “Bayesian Spatial Modelling with R-INLA.” Journal of Statistical Software 63 (February): 1–25. https://doi.org/10.18637/jss.v063.i19.
Lindgren, Finn, Håvard Rue, and Johan Lindström. 2011. “An Explicit Link Between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 (4): 423–98. https://doi.org/10.1111/j.1467-9868.2011.00777.x.
Moraga, Paula. 2023. Spatial Statistics for Data Science: Theory and Practice with R. Chapman & Hall/CRC Data Science Series.
Rue, Håvard, Sara Martino, and Nicolas Chopin. 2009. “Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71 (2): 319–92. https://doi.org/10.1111/j.1467-9868.2008.00700.x.
Simpson, Daniel, Håvard Rue, Andrea Riebler, Thiago G. Martins, and Sigrunn H. Sørbye. 2017. “Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors.” Statistical Science 32 (1): 1–28. https://doi.org/10.1214/16-STS576.
Simpson, D., J. B. Illian, F. Lindgren, S. H. Sørbye, and H. Rue. 2016. “Going Off Grid: Computationally Efficient Inference for Log-Gaussian Cox Processes.” Biometrika 103 (1): 49–70. https://doi.org/10.1093/biomet/asv064.