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The Bayesian Computational Statistics and Modeling group uses Bayesian methods to solve real-life challenges on a computationally efficient framework. We develop fundamental theory, methodology, computational frameworks as well as the end-user implementation in the R-INLA package under the leadership and guidance of Prof. Haavard Rue (also see here). All our research is very exciting since we develop statistical products for use by scientists and practitioners, as complimentary to all methodology. Some expository articles on important applications of our work can be found in KAUST Discovery

Research

The Bayesian Computational Statistics and Modeling group (BayesComp) at King Abdullah University of Science and Technology (KAUST) involves research focusing on the modeling, design, optimization, and performance analysis of Bayesian modeling techniques. The group strives to develop generic solutions to real-life challenges for use by scientists from all fields. A particular feature of all approaches is the ability to scale to large datasets. The methodologies and computational advances developed by the group is implemented in the R-INLA package (see http://www.r-inla.org). Current focus areas

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Bayesian Computational Statistics and Modeling Research Group

Professor Haavard Rue leads the so-called BAYESCOMP research group, which stands for Bayesian Computational Statistics and Modeling Research Group 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

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