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Dear Allstat, Please see below an announcement regarding a 2-day INLA course taking place at the Swiss TPH. Best, Alex Course: INLA and SPDEs Description: A two day course on approximate Bayesian inference for latent Gaussian models (LGM) using integrated nested Laplace approximations (INLA) will take place at the Swiss Tropical and Public Health Institute in Basel, Switzerland on 28 February and 1 March 2013. The course will be given by Dr. Daniel Simpson, Department of Mathematical Sciences, Norwegian University of Science and Technology. Content: LGM's are perhaps the most commonly used class of models in statistical applications. It includes, among others, most of (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatio-temporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. The concept of LGM is intended for the modeling stage, but turns out to be extremely useful when doing inference as models, listed above, can be treated in a unified way and using the *same* algorithm and software tool. The approach to (approximate) Bayesian inference is to use INLA. Using this new tool, very accurate approximations to the posterior marginals can be directly computed. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, these approximations provide more precise estimates in seconds or minutes. The course will have an emphasis on spatio-temporal modeling through the stochastic partial differential equations (SPDE) approach. Throughout these lectures, the methods described will be discussed and illustrated with a number of examples ranging from classical 'BUGS' examples to real-life complex spatial (and maybe even spatio-temporal) data sets. It is, therefore, suggested that participants bring a laptop with the most recent version of R and a working version of the INLA package. Day 1: Day 2: -LGMs and Gaussian Markov random fields -theory behind INLA -R-INLA package -details on Markovian spatial models -SDPEs and the link with GMRFs -R-INLA for spatial (or spatio-temporal) models Location: In Basel, Switzerland. Room will be announced. Organized by: Dr. Penelope Vounatsou, Alex Karagiannis, Epidemiology and Public Health department, Swiss Tropical and Public Health Institute. Target audience: Graduates of MSc in Statistics or Mathematics with training in Bayesian inference. Fee: None. Registration: Required as the number of participants is limited. Contact: [log in to unmask] Further information: http://www.r-inla.org/ Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Series B Stat Methodol 71: 319-392. Lindgren F, Rue H, Lindström J (2011) An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J R Stat Soc Series B Stat Methodol 73: 423-498. -------------------------------------------------------------------------- This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error, please notify us immediately by reply e-mail and delete this message from your system. -------------------------------------------------------------------------- You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.