Imperial College London

ProfessorMartaBlangiardo

Faculty of MedicineSchool of Public Health

Chair in Biostatistics
 
 
 
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Contact

 

m.blangiardo Website

 
 
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Location

 

528Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Blangiardo:2013:10.1016/j.sste.2013.07.003,
author = {Blangiardo, M and Cameletti, M and Baio, G and Rue, H},
doi = {10.1016/j.sste.2013.07.003},
journal = {Spat Spatiotemporal Epidemiol},
pages = {39--55},
title = {Spatial and spatio-temporal models with R-INLA.},
url = {http://dx.doi.org/10.1016/j.sste.2013.07.003},
volume = {7},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.
AU - Blangiardo,M
AU - Cameletti,M
AU - Baio,G
AU - Rue,H
DO - 10.1016/j.sste.2013.07.003
EP - 55
PY - 2013///
SP - 39
TI - Spatial and spatio-temporal models with R-INLA.
T2 - Spat Spatiotemporal Epidemiol
UR - http://dx.doi.org/10.1016/j.sste.2013.07.003
UR - https://www.ncbi.nlm.nih.gov/pubmed/24377114
VL - 7
ER -