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
3.2 Derivation of gradients and Fisher information for SGRLD
In order to compute the log-likelihood, we need the following quantities
3.2.1 Mean parameters
The gradient of the minibatch log-likelihood with respect to the mean parameters β is
3.2.2 Covariance parameters
This paper is available on arxiv under CC BY 4.0 DEED license.
