Computationally efficient nonstationary nearest-neighbor Gaussian process models using data-driven techniques
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Publication:6626105
DOI10.1002/env.2571zbMATH Open1545.62826MaRDI QIDQ6626105
Ahmad A. Hanandeh, Pulong Ma, Bledar A. Konomi, Emily L. Kang
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
binary treeMarkov chain Monte Carlo (MCMC)Bayesian hierarchical modelinglarge data setsnonstationary covariance functionTOMS ozone data
Cites Work
- Unnamed Item
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Comparing composite likelihood methods based on pairs for spatial Gaussian random fields
- Covariance tapering for multivariate Gaussian random fields estimation
- Composite likelihood inference for multivariate Gaussian random fields
- Multi-output local Gaussian process regression: applications to uncertainty quantification
- Nonstationary covariance models for global data
- Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV.
- Estimating deformations of isotropic Gaussian random fields on the plane
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
- Multiresolution models for nonstationary spatial covariance functions
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- A Full Scale Approximation of Covariance Functions for Large Spatial Data Sets
- Approximating Likelihoods for Large Spatial Data Sets
- Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
- A dimension-reduced approach to space-time Kalman filtering
- Approximate Likelihood for Large Irregularly Spaced Spatial Data
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- A general framework for Vecchia approximations of Gaussian processes
- Bayesian nonstationary spatial modeling for very large datasets
- Spatio-temporal change of support with application to American community survey multi-year period estimates
- Permutation and Grouping Methods for Sharpening Gaussian Process Approximations
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