Update: As of March 2014 I am no longer a Research Fellow at NTNU, see here.
I am currently a PhD candidate at NTNU within the statistics group. And within the statistics group, we have a smaller group led by Håvard Rue that are sometimes informally called the INLA group. Now, what is INLA?
INLA stands for Integrated Nested Laplace Approximations and is an approach proposed by (Rue et al., 2009) to perform approximate fully Bayesian inference on the class of latent Gaussian models (LGMs). INLA makes use of deterministic nested Laplace approximations and, as an algorithm tailored to the class of LGMs, it provides a faster and more accurate alternative to simulation-based MCMC schemes.
The range of models that belong to the LGM family, and hence can be handled by INLA, are enormous and represent many of the most commonly used models by the applied community. Our group provide an R package called
INLA that allow the user to specify complex models using the easy to use
formula syntax available in R.
Please visit our page for more information about INLA and its R package. The website contains several worked out examples, papers and even the complete source code of the project. I also plan to write, among other things, about INLA on my blog posts from time to time.
Rue, H. and Martino, S. and Chopin, N., 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B(Statistical Methodology).