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Dist2.m
Figure4.m
GaussMx.m
GaussMxnd.m
MGPR.pdf
README.md
Test_Kern_Std.m
Test_fd_MultiScale_F1i.m
Train_Kern_Std.m
Train_fd_MultiScale_F1c.m
hcluster0.m
optLML_Multiscale2.m

README.md

MGPR_2015

The code in this repository is intended to replicate Figure 4 in the paper "Efficient Multiscale Gaussian Process Regression Using Hierarchial Clustering." Questions and concerns about the code should be directed towards Ze Jia Zhang (zzejia at umich dot edu).

Files

  • MGPR.pdf: preprint PDF version of the paper.
  • Figure4.m: script to produce the desired figure.
  • Train_Kern_Std.m: training algorithm for standard GPR.
  • Test_Kern_Std.m: testing algorithm for standard GPR.
  • Train_fd_Multiscale_F1i.m: training algorithm for MGPR.
  • Test_fd_Multiscale_F1c.m: testing algorithm for MGPR.
  • optLML_Multiscale2.m: optimization container for hyperparameters.
  • hcluster0.m: clustering algorithm for MGPR.
  • GaussMx.m, GaussMxnd.m: construction of Gaussian kernels.
  • Dist2.m: compute pairwise distance between points.