Spatial modeling with R-INLA: a review
From MaRDI portal
Publication:6602213
DOI10.1002/WICS.1443zbMATH Open1544.62006MaRDI QIDQ6602213
Elias Krainski, Håvard Rue, Andrea Riebler, D. Simpson, Finn Lindgren, David Bolin, J. B. Illian, Geir-Arne Fuglstad, Haakon Bakka
Publication date: 11 September 2024
Published in: Wiley Interdisciplinary Reviews. WIREs Computational Statistics (Search for Journal in Brave)
spatial statisticsstochastic partial differential equationssparse matricesapproximate Bayesian inferenceGaussian Markov random fieldsLaplace approximations
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Disease mapping and spatial regression with count data
- Space-time smoothing of complex survey data: small area estimation for child mortality
- Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
- On the validity of commonly used covariance and variogram functions on the sphere
- Bayesian adaptive smoothing splines using stochastic differential equations
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Interpolation of spatial data. Some theory for kriging
- Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales
- Penalising model component complexity: a principled, practical approach to constructing priors
- INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles
- A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
- Estimation and extrapolation of time trends in registry data -- borrowing strength from related populations
- A primer on disease mapping and ecological regression using \({\mathtt{INLA}}\)
- Going off grid: computationally efficient inference for log-Gaussian Cox processes
- Approximate Bayesian Inference for Survival Models
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Geostatistical Modelling Using Non-Gaussian Matérn Fields
- Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping
- Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
- On the Second‐Order Random Walk Model for Irregular Locations
- Log Gaussian Cox Processes
- Fitting Gaussian Markov Random Fields to Gaussian Fields
- Markov Point Processes and Their Applications
- Assessing the Impact of a Movement Network on the Spatiotemporal Spread of Infectious Diseases
- Gaussian Markov Random Fields
- Applied Spatial Data Analysis with R
- Constructing Priors that Penalize the Complexity of Gaussian Random Fields
- Spatial and Spatio‐temporal Bayesian Models with R‐INLA
- Excursion and Contour Uncertainty Regions for Latent Gaussian Models
- Stomach cancer incidence in Southern Portugal 1998–2006: A spatio‐temporal analysis
- Statistical Analysis and Modelling of Spatial Point Patterns
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
- ON STATIONARY PROCESSES IN THE PLANE
- Spatial modeling with system of stochastic partial differential equations
- Bayesian spatial modelling for high dimensional seismic inverse problems
- Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution
Related Items (8)
A diffusion-based spatio-temporal extension of Gaussian Matérn fields ⋮ Past, present and future of software for Bayesian inference ⋮ A scalable approach for short-term disease forecasting in high spatial resolution areal data ⋮ Advances in statistical modeling of spatial extremes ⋮ Spatiotemporal reconstructions of global CO\(_2\)-fluxes using Gaussian Markov random fields ⋮ Approximation of Bayesian Hawkes process with \texttt{inlabru} ⋮ A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets ⋮ A flexible generalized Poisson likelihood for spatial counts constructed by renewal theory, motivated by groundwater quality assessment
This page was built for publication: Spatial modeling with R-INLA: a review
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6602213)
