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
Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA [PDF]
Bayesian Analysis, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dawkins, LC +5 more
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Numerical Recipes for Landslide Spatial Prediction Using R-INLA [PDF]
, 2019 The geomorphological community typically assesses the landslide susceptibility at the catchment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates.
Lombardo, Luigi +2 more
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Spatial Data Analysis with R-INLA with Some Extensions
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
doaj +1 more source
Bayesian joint models with INLA exploring marine mobile predator-prey and competitor species habitat overlap [PDF]
, 2017 EPSRC grant Ecowatt 2050 EP/K012851/1 ACKNOWLEDGMENTS We would like to thank the associate editor and the anonymous reviewers for their useful and constructive suggestions which led to a considerable improvement of the manuscript.
Dominicis, Michela De +5 more
core +2 more sources
A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA [PDF]
Environmetrics, 2020 AbstractIn air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions.
C. Forlani +4 more
openaire +6 more sources
Modelación espacio-temporal de la incidencia acumulada de COVID-19 en municipios de Chiapas
ESPACIO I+D: Innovación más Desarrollo, 2020 El trabajo tiene como finalidad analizar la evolución de la tasa de incidencia acumulada de COVID-19 en los municipios de Chiapas, entre los meses de Febrero a Julio del año 2020, a partir de la aplicación de tres modelos bayesianos jerárquicos espacio ...
Gerardo Núñez Medina
doaj +1 more source
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
openaire +3 more sources
A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia [PDF]
, 2013 This paper outlines a methodology for semi-parametric spatio-temporal modelling of data which is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data
Box G. E. P. +6 more
core +2 more sources