An additive approximate Gaussian process model for large spatio‐temporal data
Author:
Affiliation:
1. Statistical and Applied Mathematical Sciences Institute, Duke University Durham North Carolina
2. Division of Statistics and Data Science, Department of Mathematical SciencesUniversity of Cincinnati Cincinnati Ohio
Publisher
Wiley
Subject
Ecological Modelling,Statistics and Probability
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2569
Reference29 articles.
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3. Gaussian predictive process models for large spatial data sets;Banerjee S.;Journal of the Royal Statistical Society: Series B (Statistical Methodology),2008
4. Multi‐output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification;Bilionis I.;Journal of Computational Physics,2013
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