Probabilistic forecasts of arctic sea ice thickness
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Publication:2163520
DOI10.1007/s13253-021-00480-0OpenAlexW3213811478WikidataQ114220112 ScholiaQ114220112MaRDI QIDQ2163520
Adrian E. Raftery, Hannah M. Director, Cecilia M. Bitz, Peter A. Gao
Publication date: 10 August 2022
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13253-021-00480-0
spatial statisticsGaussian processstochastic partial differential equationintegrated nested Laplace approximationspatio-temporal forecasting
Uses Software
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Penalising model component complexity: a principled, practical approach to constructing priors
- Bayesian inference of spatio-temporal changes of arctic sea ice
- Probabilistic forecasting of the Arctic sea ice edge with contour modeling
- A case study competition among methods for analyzing large spatial data
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Penalised Complexity Priors for Stationary Autoregressive Processes
- A Full Scale Approximation of Covariance Functions for Large Spatial Data Sets
- Gaussian Markov Random Fields
- Constructing Priors that Penalize the Complexity of Gaussian Random Fields
- Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Excursion and Contour Uncertainty Regions for Latent Gaussian Models