INLA short Course: Bayesian Modeling via integrated Laplace Approximation
Presenter: Dr Daniel Simpson (Dept of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway)
Dates: April 1st – 2nd
this two day free course will cover basic principles of Laplace approximation (LA) to posterior distributions and their application in Bayesian modeling via the use of the R package INLA (www.r-inla.org). INLA potentially provides a major advance in ability to handle complex models with large predictor spaces. LA does not require convergence checking (unlike McMC) and often is MUCH faster in run-time than WinBUGS equivalent programs.
Attendees: require to attend with laptop with recent R version and INLA sourced from R using source("http://www.math.ntnu.no/inla/givemeINLA.R")
Notification: as this course is free we need to know who intends to attend so that accommodation is appropriate. We will limit numbers to 25 maximum. Please let June Watson know if you intend to attend (email: email@example.com)
Queries to: Dr Andrew Lawson (firstname.lastname@example.org)
In these lectures, 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
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.
During the first day, I will discuss Latent Gaussian Models;