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 The SG-MCMC Algorithm
In this section we first review the general SG Langevin dynamics method and then present the proposed algorithm based on the Vecchia approximation.
3.1 SG Langevin Dynamics
In order to assure convergence to the true posterior the step sizes must satisfy
This paper is available on arxiv under CC BY 4.0 DEED license.
