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haavard.rue@kaust.edu.sa Bayesian Computational Statistics and Modeling
H. Rue, S. Martino, and N. Chopin. “Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with dis-cussion)”. In: Journal of the Royal Statistical Society, Series B 71.2 (2009), pp. 319–392.
F. Lindgren, H. Rue, and J. Lindström. “An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach (with discussion)”. In: Journal of the Royal Statistical Society, Series B 73.4 (2011), pp. 423–498.
D. Simpson, J. Illian, F. Lindgren, S. Sørbye, and H. Rue. “Going off grid: Com-putational efficient inference for log-Gaussian Cox processes”. In: Biometrika 103.1 (2016). (doi: 10.1093/biomet/asv064), pp. 1–22.
D. P. Simpson, H. Rue, T. G. Martins, A. Riebler, and S. H. Sørbye. Penalising model component complexity: A principled, practical approach to constructing priors. arXiv:1403.4630 (revised in 2015). NTNU, Trondheim, Norway, 2014.
H. Rue and L. Held. Gaussian Markov Random Fields: Theory and Applications. Vol. 104. Monographs on Statistics and Applied Probability. London: Chapman & Hall, 2005.