Document Detail

Spatial and spatio-temporal models with R-INLA.
MedLine Citation:
PMID:  24377114     Owner:  NLM     Status:  In-Process    
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.
Marta Blangiardo; Michela Cameletti; Gianluca Baio; Håvard Rue
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Spatial and spatio-temporal epidemiology     Volume:  7     ISSN:  1877-5853     ISO Abbreviation:  Spat Spatiotemporal Epidemiol     Publication Date:  2013 Dec 
Date Detail:
Created Date:  2013-12-30     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101516571     Medline TA:  Spat Spatiotemporal Epidemiol     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  39-55     Citation Subset:  IM    
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