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Published September 23, 2021 | Version v1
Software Open

Code for "Domain decomposition approach for fast Gaussian process regression of large spatial datasets"

  • 1. Florida State University
  • 2. Chinese University of Hong Kong
  • 3. Texas A&M University

Description

This is the R code for reproducing the results in the paper, Park, Huang,  and Ding, 2011, “Domain decomposition approach for fast Gaussian process regression of large spatial datasets,” Journal of Machine Learning Research, Vol. 12, pp. 1697 – 1728. The datasets used are also included in the zip file.

There is a companion paper in the Journal of Machine Learning Research, discussing a toolbox called GPLP. The toolbox and the supporting documents are accessible at MLOSS (Machine Learning Open Source Software) project website http://mloss.org/revision/view/990/.

Files

J40_code.zip

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Additional details

References

  • Park, Huang, and Ding, 2011, "Domain decomposition approach for fast Gaussian process regression of large spatial datasets," Journal of Machine Learning Research, Vol. 12, pp. 1697 – 1728.
  • Park, Huang, and Ding, 2012, "GPLP: a local and parallel computation toolbox for Gaussian process regression," Journal of Machine Learning Research, Vol. 13, 775-779.