A statistical framework to combine multivariate spatial data and physical models for hurricane surface wind prediction
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Publication:2259845
DOI10.1198/108571108X276473zbMath1306.62274OpenAlexW1979433275MaRDI QIDQ2259845
Publication date: 5 March 2015
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1198/108571108x276473
Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)
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Uses Software
Cites Work
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- Nonstationary multivariate process modeling through spatially varying coregionalization
- Sampling-Based Approaches to Calculating Marginal Densities
- Model choice: a minimum posterior predictive loss approach
- Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds
- Bayesian Measures of Model Complexity and Fit
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- SPACE–TIME MODELLING OF SYDNEY HARBOUR WINDS
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