Fast Spatial Gaussian Process Maximum Likelihood Estimation via Skeletonization Factorizations

@article{Minden2017FastSG,
  title={Fast Spatial Gaussian Process Maximum Likelihood Estimation via Skeletonization Factorizations},
  author={Victor Minden and Anil Damle and Kenneth L. Ho and Lexing Ying},
  journal={Multiscale Model. Simul.},
  year={2017},
  volume={15},
  pages={1584-1611}
}
Maximum likelihood estimation for parameter fitting given observations from a Gaussian process in space is a computationally demanding task that restricts the use of such methods to moderately sized datasets. We present a framework for unstructured observations in two spatial dimensions that allows for evaluation of the log-likelihood and its gradient (i.e., the score equations) in $\tilde O(n^{3/2})$ time under certain assumptions, where $n$ is the number of observations. Our method relies on… Expand
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