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Håvard Rue

Professor, Statistics

Chair, Statistics Program

Principal Investigator, Bayesian Computational Statistics and Modeling

Computer, Electrical and Mathematical Science and Engineering Division
Email icon haavard.rue@kaust.edu.sa

Do one thing, and do it well

Program Affiliations

Biography

Professor Rue earned his Ph.D. in 1993 from the Norwegian University of Science and Technology. He began his academic career at the same institution in 1994 and was promoted to full professor in 1997. He has also held adjunct positions at the Norwegian Computing Center and the Arctic University of Norway. Rue is an elected member of the Norwegian Academy of Science and Letters, the Royal Norwegian Society of Science and Letters, the Norwegian Academy of Technological Sciences and the International Statistical Institute.

Upon joining KAUST in 2017, Rue established the Bayesian Computational Statistics & Modeling research group. The group develops efficient Bayesian inference schemes and tools to improve Bayesian inference and modeling using latent Gaussian models. He received the Guy Medal in Silver from the Royal Statistical Society in 2021 for his groundbreaking work in this area.

Research Interests

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—a project aimed at providing a practical way to analyze latent Gaussian models at extreme data scales using approximate Bayesian analysis. The work also includes efforts to model Gaussian fields with stochastic partial differential equations, which are applied to spatial statistics.
Keyword tag icon
Bayesian computational statistics bayesian methodology latent Gaussian models spatial statistics

Education Profile

  • PhD Norwegian Institute of Technology, 1993

  • MEng Norwegian Institute of Technology, 1988

Awards and Recognitions

Publications

  • 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.

Research Areas

Multimedia