Post date: Dec 5, 2012 8:14:47 AM
Copy from a posting on the allstat email-list 3rd December
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We have been very fortunate in being able to host a workshop on R-INLA, presented by Dr Dan Simpson, from NTNU at Trondheim, Norway. Dan works with Professor Harvard Rue, who co-wrote INLA. He works on fast computational methods for sparse GMRFs. Dan completed his PhD at QUT under Ian Turner's supervision.
THE COURSE WILL BE HELD THIS THURSDAY, WITH OPPORTUNITY FOR ONE-ON-ONE MEETINGS WITH WORKSHOP PARTICIPANTS ON FRIDAY.
Registration is free, but you need to tell Sam (sj.clifford@student.qut.edu.au) that you are coming. You will need to bring a laptop.
DATE: Thursday 6th December: course
Friday 7th December: one-on-one meetings with course participants
TIMES: Thursday 9-12, 1.30-4.30
Friday: as negotiated
LOCATION: P413A
BRING: laptop with R (and the ability to install packages if possible)
RSVP: as soon as possible! by 9am Thursday... to Sam Clifford sj.clifford@student.qut.edu.au
FOOD: Morning and afternoon tea will be provided. Lunch will not be provided.
COURSE ABSTRACT
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
to be extremely useful 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 new
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.
During the morning sessions, I will discuss Latent Gaussian Models;
the theory required to understand the INLA method (including details
on Gaussian Markov random fields and fast computations of those using
sparse matrix algorithms); and the R-INLA program. During the
afternoon sessions I will discuss in detail issues surrounding the
computationally efficient Markovian spatial models described by
Lindgren et al (JRSS B, 2011) and show how the R-INLA package can be
used to solve these models. Throughout these lectures, the methods
described will be illustrated with a number of examples ranging from
classical 'BUGS' examples to real-life complex spatial (and maybe even
spatiotemporal) data sets. It is suggested that participants bring a
laptop with the most recent version of R and a working version of INLA
so that they can 'play along'.