Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA [PDF]
Spatial and Spatio-temporal EpidemiologyWeekly county-level COVID-19 mortality and emergency department (ED) visits data are critical data sources for understanding COVID-19 trends, but subject to reporting delays, sampling variability, potential instability and concerns due to statistical reliability as well as data suppression due to small numbers and the need to protect personally ...
Diba Khan +7 more
openaire +3 more sources
Computationally efficient Bayesian inference for semi-parametric joint models of competing risks survival and skewed longitudinal data using integrated nested Laplace approximation [PDF]
BMC Medical Research MethodologyBackground Joint modeling is widely used in medical research to properly analyze longitudinal biomarkers and survival outcomes simultaneously and to guide appropriate interventions in public health.
Melkamu Molla Ferede +2 more
doaj +2 more sources
Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models [PDF]
MethodsXCharacterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks.
Kristin J. Bondo +2 more
doaj +2 more sources
Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA [PDF]
, 2020 Exposure to air pollution in the form of fine particulate matter (PM2.5) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of PM2.5 using mobile phone applications and ...
G Shaddick (21866612) +5 more
core +6 more sources
Laplace approximation for conditional autoregressive models for spatial data of diseases
MethodsX, 2022 Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases.
Guiming Wang
doaj +1 more source
Bayesian Multivariate Spatial Models for Lattice Data with INLA
Journal of Statistical Software, 2021 The INLAMSM package for the R programming language provides a collection of multivariate spatial models for lattice data that can be used with the INLA package for Bayesian inference.
Francisco Palmí-Perales +2 more
doaj +1 more source
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
Using INLA to fit a complex point process model with temporally varying effects – a case study [PDF]
, 2012 Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses
Soerbye, S +7 more
core +2 more sources
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
openaire +4 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