Professor Rue's research interests lie in computational Bayesian statistics and Bayesian methodology such as priors, sensitivity and robustness. His main body of research is built around the R-INLA project (www.r-inla.org), which aims to provide a practical tool for approximate Bayesian analysis of latent Gaussian models, often at extreme data scales. This project also includes efforts to use stochastic partial differential equations to represent Gaussian fields, for the use in spatial statistics.
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.