Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework
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Publication:2242020
DOI10.1016/j.csda.2021.107240OpenAlexW3154783044MaRDI QIDQ2242020
Publication date: 9 November 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1906.00364
Uses Software
Cites Work
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Cross-covariance functions for multivariate geostatistics
- A modeling approach for large spatial datasets
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Bayesian computing with INLA: new features
- A spatio-temporal downscaler for output from numerical models
- Going off grid: computationally efficient inference for log-Gaussian Cox processes
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Generalized Hierarchical Multivariate CAR Models for Areal Data
- Bayesian Geostatistical Design
- Handbook of Spatial Statistics
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Log Gaussian Cox Processes
- On the change of support problem for spatio-temporal data
- Combining Incompatible Spatial Data
- Modeling Spatial Variation in Disease Risk
- Gaussian Markov Random Fields
- A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases
- Hierarchical Factor Models for Large Spatially Misaligned Data: A Low‐Rank Predictive Process Approach
- Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments†
- Constructing Priors that Penalize the Complexity of Gaussian Random Fields
- Generalized common spatial factor model
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm
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