10th Applied Statistics 2013
International Conference
September 22 - 25, 2013
Ribno (Bled), Slovenia

Past conferences:
Applied Statistics 2012
Applied Statistics 2011

Applied Statistics 2010
Applied Statistics 2009
Applied Statistics 2008
Applied Statistics 2007

Applied Statistics 2006
Applied Statistics 2005
Applied Statistics 2004
Conferences before 2004


  Important dates

June 1

  Acceptance note
     June 15
Sent out

  Registration and
  hotel reservation
      July 1
  Reduced early
  payment fee
      July 15



Bayesian computation with INLA

Thiago G. Martins
Department of Mathematical Sciences
Norwegian University of Science and Technology

In this tutorial, I will discuss approximate Bayesian inference for a class of models named latent Gaussian models (LGM). 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 spatiotemporal 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 usefull when doing inference as we can treat models listed above in a unified way and using the same algorithm and software tool. Our approach to (approximate) Bayesian inference, is to use integrated nested Laplace approximations (INLA). Using this tool, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

I will introduce the class of latent Gaussian models, describe the "big picture" of the INLA algorithm and introduce the r-INLA package. This is intended to be an applied tutorial, so I will focus on the use of the package on a variety of examples rather than the theory and implementation details behind INLA.

Keywords: Approximate Bayesian inference, INLA, Latent Gaussian models

 Applied Statistics 2013      http://conferences.nib.si/AS2013                                e-mail: info.AS@nib.si