Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets
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Publication:3391136
DOI10.1080/10618600.2017.1356324OpenAlexW2409626527MaRDI QIDQ3391136
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1605.08898
Gaussian process modelslikelihood approximationMatérn covariance functionstatistical efficiencysoil moisture
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Uses Software
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Advances and challenges in space-time modelling of natural events. Papers based on the presentations at the international spring school, Toledo, Spain, March 2010.
- Interpolation of spatial data. Some theory for kriging
- Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV.
- SYMMETRIC GAUGE FUNCTIONS AND UNITARILY INVARIANT NORMS
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Fitting Gaussian Markov Random Fields to Gaussian Fields
- A Full Scale Approximation of Covariance Functions for Large Spatial Data Sets
- Approximating Likelihoods for Large Spatial Data Sets
- Gaussian Markov Random Fields
- Conjugate Gradient Methods for Toeplitz Systems
- A dimension-reduced approach to space-time Kalman filtering
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
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