Bayesian hierarchical models for the combination of spatially misaligned data: a comparison of melding and downscaler approaches using INLA and SPDE
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Publication:6656015
DOI10.1007/s13253-023-00559-wMaRDI QIDQ6656015
Publication date: 31 December 2024
Published in: Journal of Agricultural, Biological and Environmental Statistics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Homogenization of climate data: review and new perspectives using geostatistics
- Spatio-temporal modeling of particulate matter concentration through the SPDE approach
- A spatio-temporal downscaler for output from numerical models
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- Bayesian computation: a summary of the current state, and samples backwards and forwards
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