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ALLSTAT  December 2012

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Subject:

Course on Bayesian inference for latent Gaussian models using INLA

From:

Jan van de Kassteele <[log in to unmask]>

Reply-To:

Jan van de Kassteele <[log in to unmask]>

Date:

Thu, 6 Dec 2012 13:17:38 +0000

Content-Type:

text/plain

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text/plain (56 lines)

Course on Bayesian inference for latent Gaussian models using INLA

On January 24, 2013, a one day course on Bayesian inference for latent Gaussian models using INLA will be organized at the National Institute for Public Health and the Environment – RIVM, the Netherlands. The course will be presented by Håvard Rue of the Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway. 

Course information

This one day course will provide an overview of the INLA approach and its applications.

INLA (Integrated Nested Laplace Approximations) revolutionizes the computer performance of Bayesian inference for latent Gaussian models. Using this recent tool, very accurate approximations to the posterior marginals can be computed directly. The main benefit of these approximations is computational: where MCMC algorithms need hours or days to run, INLA provides precise estimates in seconds or minutes. Another advantage is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way.

Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical modeling applications. It includes, among others:
- (generalized) linear mixed models 
- (generalized) additive models
- smoothing spline models and semi-parametric regression models
- spatial models 
- temporal models 
- log-Gaussian Cox processes 
- geostatistical models

Possible applications are found in:
- longitudinal data analysis 
- time series analysis
- disease mapping
- spatial survival analysis
- spatial association studies
- point processes
- interpolation of spatial data

The course is intended for statisticians, epidemiologists, students and other researchers who want to model their data using LGMs. Basic knowledge of Bayesian statistics and generalized linear and additive models is recommended.

More information on INLA can be found at http://www.r-inla.org/
Reference: Rue H, Martino S and Chopin N (2009). Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71, 319–392

Practical information

Organizers
Jan van de Kassteele & Caroline Ameling
National Institute for Public Health and the Environment – RIVM

Date and time
January 24, 2013. 9.00 – 17.00h.

Location
National Institute for Public Health and the Environment – RIVM, Bilthoven, the Netherlands.

Fee
50 euro. Registration is required. Lunch and course material are included. Payment by invoice.

Registration
Registration is done by e-mailing [log in to unmask] Deadline for registration is January 18, 2013.

You may leave the list at any time by sending the command

SIGNOFF allstat

to [log in to unmask], leaving the subject line blank.

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