An Introduction to Bayesian computing with INLA

Speaker:
Håvard Rue, King Abdullah University of Science and Technology, Saudi Arabia

Abstract: 
In this talk I will give an introduction to Bayesian computing with INLA and the R-INLA package. INLA is a way to do approximate Bayesian inference for a certain class of model: latent Gaussian models (LGM). LGM's are extensive used in statistical modelling, and the INLA approach to Bayesian inference falls naturally out of this structure.  The R-INLA package have quite a few non-standard features in the model definition using the formula-statement, which really extends the class of models accessible from within R.

Biography
Havard Rue is professor in statistics at at KAUST (kaust.edu.sa), where he is building up a new research group. Previously he was professor in statistics at the Department of Mathematical Sciences, Norwegian University of Science and Technology. His research interest includes Bayesian computing and spatial statistics, which is summarized in the R-INLA package, see www.r-inla.org. He has been an associate editor for JRSS series-B, Scandinavian Journal of Statistics, Statistic Surveys, Annals of Statistics and Environmetrics. His main research interest has been Gaussian Markov random fields (GMRF) models, and with Leonhard Held he has written a monograph on the subject published by Chapman & Hall. GMRFs is also a main ingredient doing (fast and accurate) approximate Bayesian analysis for latent Gaussian models integrated as a discussion paper for JRSS series B 2009 co-authored with S. Martino and N. Chopin.

Host: Prof. Ernst Wit