[Developers] FW: seminar on INLA

[Mark Maunder]

From: Richard Methot - NOAA Federal [mailto:richard.methot at noaa.gov]
Sent: Wednesday, July 24, 2013 11:35 AM
To: Mark Maunder; Jim Ianelli - NOAA Federal; James Thorson - NOAA Federal; Allan Hicks - NOAA Federal; Ian Taylor - NOAA Federal; Athol Whitten
Subject: seminar on INLA


Dr. Janine Illian from the Centre for Research into Ecological and

Environmental Modelling (CREEM) at the University of St. Andrews, UK

will be visiting next week and has kindly agreed to give a NMML seminar

(NMML conference room, Wednesday, July 31, at noon). I'll send out an

email reminder next week, but hope you can join us. As Dr. Illian's

topic is not limited to marine mammal applications, please feel free to

forward this announcement to other NMFS/AFSC scientists who may be

interested.

FITTING COMPLEX MODELS IN INLA - DEVELOPMENTS AND EXTENSIONS

Integrated nested Laplace approximation (INLA) may be used to fit a
large class of (complex) statistical models. While MCMC methods use
stochastic simulations for estimation, integrated nested Laplace
approximation (INLA) is based on deterministic approximations where
there are no convergence issues. INLA is a very accurate and
computationally superior alternative to MCMC and may be used to fit a
large class of models, latent Gaussian models. Since INLA is fast,
complex modelling has become greatly facilitated and has also become
more accessible to non-specialists. In addition, due to the fact that
the fitting approach is embedded in a large and general class of
statistical models, very general types of models may be considered. This
allows us a lot more flexibility in the choice of model than previously
- and hence the models to capture interesting aspects of the data and
consequently the system they are relevant for. In the context of spatial
statistics, for example, we can now fit models to spatial point patterns
of high dimensionality, replicated point patterns, hierarchically marked
point patterns etc. In many cases, analysing these data sets with MCMC
approaches would be very cumbersome and computationally prohibitive. The
INLA-methodology has been implemented in C, and the associated numerical
calculations and algorithms rely on an efficient implementation of
numerical procedures for Gaussian Markov random fields (GMRF), in
particular the algorithms in the C-library GMRFLib. However, most users
do not need to worry about this, as the INLA-methodology has been made
accessible through a user-friendly R-library, R-INLA, described and
available for download at www.r-inla.org<http://www.r-inla.org>. Specifying and fitting models
using R-INLA is just as easy as applying standard routines in R, for
example fitting generalised linear models, and it also provides great
flexibility with regard to the models that may be fitted. In order to
illustrate INLA's versatility I will discuss a range of spatial and
non-spatial examples and present a number of recent developments. This
concerns generalisations of the methodology as well as new functionality
within the R-INLA library.

--
Richard D. Methot Jr. Ph.D.
NOAA Fisheries - Science Advisor for Stock Assessments
Office: 206-860-3365
Mobile: 301-787-0241
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