Results 11 to 20 of about 3,465 (151)
Spatio‐temporal data integration for species distribution modelling in R‐INLA
Methods in Ecology and EvolutionSpecies distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space.
Fiona M. Seaton +2 more
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Bayesian Spatial Modelling with R-INLA [PDF]
Journal of Statistical Software, 2015 The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally ...
Finn Lindgren, Håvard Rue
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Addressing spatial confounding in geostatistical regression models: An R‐INLA approach
Methods in Ecology and EvolutionSpatial confounding, which has been studied extensively in recent years, can explain inconsistencies between results obtained by regression models with and without spatial modelling.
Jérémy Lamouroux +4 more
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Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA.
Int J Hyg Environ Health, 2021 Spatio-temporal models of ambient air pollution can be used to predict pollutant levels across a geographical region. These predictions may then be used as estimates of exposure for individuals in analyses of the health effects of air pollution. Integrated nested Laplace approximations is a method for Bayesian inference, and a fast alternative to ...
Wright N +5 more
europepmc +4 more sources
Competing risks joint models using R-INLA [PDF]
Statistical Modelling, 2020 The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause ...
Van Niekerk, Janet +2 more
openaire +3 more sources
Spatial modeling with R‐INLA: A review [PDF]
WIREs Computational Statistics, 2018 Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well.
Bakka, Haakon +8 more
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Spatial and spatio-temporal models with R-INLA [PDF]
Spatial and Spatio-temporal Epidemiology, 2013 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
Blangiardo, Marta +3 more
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Joint posterior inference for latent Gaussian models with R-INLA
Journal of Statistical Computation and Simulation, 2022 33 pages, 11 ...
Cristian Chiuchiolo +2 more
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Bayesian Model Averaging with the Integrated Nested Laplace Approximation
Econometrics, 2020 The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent ...
Virgilio Gómez-Rubio +2 more
doaj +1 more source
Revista Latinoamericana de Población, 2020 El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace ...
Gerardo Núñez Medina
doaj +8 more sources