A hierarchical spatiotemporal statistical model motivated by glaciology
From MaRDI portal
Publication:2009141
DOI10.1007/s13253-019-00367-1zbMath1427.86011arXiv1811.08472OpenAlexW2950028484WikidataQ127704538 ScholiaQ127704538MaRDI QIDQ2009141
Håvard Rue, Alexander H. Jarosch, Finnur Pálsson, Christopher K. Wikle, Birgir Hrafnkelsson, Guðfinna Aðalgeirsdóttir, Giri Gopalan
Publication date: 27 November 2019
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1811.08472
Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Geostatistics (86A32) Geodesy, mapping problems (86A30)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Fast Sampling of Gaussian Markov Random Fields
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Bayesian solution uncertainty quantification for differential equations
- Statistical analysis of differential equations: introducing probability measures on numerical solutions
- Rates of convergence of posterior distributions.
- Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models
- Bayesian Calibration of Computer Models
- Learning about physical parameters: the importance of model discrepancy
- A mathematical model of polythermal glaciers and ice sheets
- Computer Model Calibration Using High-Dimensional Output
- Asymptotic Statistics
- Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds
- Combining Field Data and Computer Simulations for Calibration and Prediction
- Gaussian Markov Random Fields
- Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
- The Bayesian Choice
- Statistical Inference for Probabilistic Functions of Finite State Markov Chains
- ON STATIONARY PROCESSES IN THE PLANE
- The elements of statistical learning. Data mining, inference, and prediction
- Random forests
- A dynamic modelling strategy for Bayesian computer model emulation
- Assimilating catchment processes with monitoring data to estimate sediment loads to the Great Barrier Reef
- Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
- Assessing exceedance of ozone standards: a space‐time downscaler for fourth highest ozone concentrations
- Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid
- Bayesian prediction of monthly precipitation on a fine grid using covariates based on a regional meteorological model