This code implements the methodology presented in
Victor Minden, Anil Damle, Kenneth L. Ho, and Lexing Ying "Fast spatial Gaussian process maximum likelihood estimation via skeletonization factorizations," arXiv:1603.08057
for maximum likelihood estimation for parameter-fitting given observations from a kernelized Gaussian process in two spatial dimensions. This code is not being actively developed, nor supported as a package.
The file example.m contains a commented demonstrative example using the included routines to perform MLE on some synthetically generated data.
Dependencies This code depends on the FLAM library
Acknowledgments The ocean data used in some examples is ICOADS, citation below:
National Climatic Data Center/NESDIS/NOAA/U.S. Department of Commerce, Data Support Section/Computational and Information Systems Laboratory/National Center for Atmospheric Research/University Corporation for Atmospheric Research, Earth System Research Laboratory/NOAA/U.S. Department of Commerce, and Cooperative Institute for Research in Environmental Sciences/University of Colorado (1984): International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 2.5, Individual Observations. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Dataset. http://dx.doi.org/10.5065/D6H70CSV. Accessed 11 Nov 2015.