Large-scale environmental data science with ExaGeoStatR
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Publication:6626535
DOI10.1002/env.2770zbMATH Open1545.62694MaRDI QIDQ6626535
Yu-Xiao Li, Hatem Ltaief, David E. Keyes, Jian Cao, Ying Sun, Sameh Abdulah, Marc G. Genton
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
predictionparameter estimationGaussian processmaximum likelihood optimizationMatérn covariance functionenvironmental application
Cites Work
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
- A comparison of spatial predictors when datasets could be very large
- Parallel inference for massive distributed spatial data using low-rank models
- Improving the performance of predictive process modeling for large datasets
- Bayesian computing with INLA: new features
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- ggplot2
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- On scale mixtures of normal distributions
- The intrinsic random functions and their applications
- Gaussian Markov Random Fields
- Statistics for Spatial Data
- Approximate Likelihood for Large Irregularly Spaced Spatial Data
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- A New Spatial Skew-Normal Random Field Model
- The Skew-normal Distribution and Related Multivariate Families*
- A general framework for Vecchia approximations of Gaussian processes
- Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets
Related Items (3)
Integrated nested Laplace approximations for large-scale spatiotemporal Bayesian modeling ⋮ Environmental data science. I ⋮ Recursive nearest neighbor co-kriging models for big multi-fidelity spatial data sets
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