Results 11 to 20 of about 15,041,214 (186)
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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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 [PDF]
Journal of Statistical Computation and Simulation, 2022 33 pages, 11 ...
Cristian Chiuchiolo +2 more
openaire +4 more sources
Spatial Data Analysis with R-INLA with Some Extensions [PDF]
Journal of Statistical Software, 2015 The integrated nested Laplace approximation (INLA) provides an interesting way of approximating the posterior marginals of a wide range of Bayesian hierarchical models.
Roger Bivand +2 more
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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
core +6 more sources
Fast and accurate Bayesian model criticism and conflict diagnostics using R‐INLA [PDF]
Stat, 2017 Bayesian hierarchical models are increasingly popular for realistic modelling and analysis of complex data. This trend is accompanied by the need for flexible, general and computationally efficient methods for model criticism and conflict detection. Usually, a Bayesian hierarchical model incorporates a grouping of the individual data points, as, for ...
Ferkingstad, Egil +2 more
core +6 more sources
Spatio‐temporal occupancy models with INLA [PDF]
Methods in Ecology and EvolutionModern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out
Jafet Belmont +3 more
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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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Spatial modelling with R-INLA: A review
, 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
core +5 more sources
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
doaj +4 more sources