About Research 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 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
Software Research The Bayescomp group is continuously developing the R-INLA software as new methodologies are devised. The homepage for the R-INLA software is http://www.r-inla.org. User support, news, case studies and frequently asked questions are facilitated on this page. For useful tutorials on using R-INLA please visit https://haakonbakka.bitbucket.io/alltopics.html.
