Authors:
(1) Mohamed A. Abba, Department of Statistics, North Carolina State University;
(2) Brian J. Reich, Department of Statistics, North Carolina State University;
(3) Reetam Majumder, Southeast Climate Adaptation Science Center, North Carolina State University;
(4) Brandon Feng, Department of Statistics, North Carolina State University.
Table of Links
1.1 Methods to handle large spatial datasets
1.2 Review of stochastic gradient methods
2 Matern Gaussian Process Model and its Approximations
3 The SG-MCMC Algorithm and 3.1 SG Langevin Dynamics
3.2 Derivation of gradients and Fisher information for SGRLD
4 Simulation Study and 4.1 Data generation
4.2 Competing methods and metrics
5 Analysis of Global Ocean Temperature Data
6 Discussion, Acknowledgements, and References
Appendix A.1: Computational Details
Appendix A.2: Additional Results
Appendix A.2: Additional Results
Maximum likelihood estimates
The results in Table-7 show that the SGFS outperforms the other methods in terms of estimation error. Compared to GpGp, the stochastic methods take at most half the time while performing twenty times more iterations.
This paper is available on arxiv under CC BY 4.0 DEED license.
