Bayesian nonstationary spatial modeling for very large datasets
From MaRDI portal
Publication:6069068
DOI10.1002/env.2200zbMath1525.62153arXiv1204.2098OpenAlexW2952060454MaRDI QIDQ6069068
Publication date: 15 December 2023
Published in: Environmetrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1204.2098
model selectionreversible-jump MCMCmassive datasetscovariance taperinglow-rank modelsfull-scale approximation
Related Items (7)
An area-specific stick breaking process for spatial data ⋮ A Fused Gaussian Process Model for Very Large Spatial Data ⋮ A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes ⋮ Nested kriging predictions for datasets with a large number of observations ⋮ Parallel inference for massive distributed spatial data using low-rank models ⋮ Bayesian nonstationary Gaussian process models via treed process convolutions ⋮ Hierarchical Bayesian modeling of ocean heat content and its uncertainty
Cites Work
- Unnamed Item
- Unnamed Item
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors
- Improving the performance of predictive process modeling for large datasets
- Statistical analysis of small-area data based on independence, spatial, non-hierarchical, and hierarchical models
- A modeling approach for large spatial datasets
- Interpolation of spatial data. Some theory for kriging
- Unimodal spectral windows
- Compactly supported correlation functions
- Spatial dynamic factor analysis
- Bayesian Regression with Multivariate Linear Splines
- Bayesian Inference for the Spatial Random Effects Model
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Partially Collapsed Gibbs Samplers
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- On Deriving the Inverse of a Sum of Matrices
- Sparse Matrices in MATLAB: Design and Implementation
- Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds
- A Full Scale Approximation of Covariance Functions for Large Spatial Data Sets
- Bayes Factors
- Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
- A dimension-reduced approach to space-time Kalman filtering
- Equation of State Calculations by Fast Computing Machines
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets
- Monte Carlo sampling methods using Markov chains and their applications
- A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities
- Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
- An adaptive Metropolis algorithm
This page was built for publication: Bayesian nonstationary spatial modeling for very large datasets
