The examples are provided by R. Cavoretto, A. De Rossi, 
E. Perracchione. Cite as 
R. Cavoretto, A. De Rossi, E. Perracchione, 
Learning with Partition of Unity-based Kriging Estimators: 
Efficient Uncertainty Computation. Available on Researchgate at
https://www.researchgate.net/profile/Emma-Perracchione

REMARK: Despite the codes work in any dimensions, the available data 
allow the reproduction only of the 1d experiments of the above paper. 

The routine MainKEPU contains examples concerning 
the computation of the KEPU interpolant and associated variance. 

To run such codes the following Matlab functions must be downloaded.

G. E. Fasshauer, DistanceMatrix.m, available at:
http://www.math.usm.edu/cschen/COS702/DistanceMatrix.m

G. E. Fasshauer, MakeSDGrid.m, available at:
http://www.math.unipd.it/~demarchi/TAA2010/MakeSDGrid.m

D. Dougherty, haltonseq.m, available at:
http://www.math.usm.edu/cschen/COS702/HW1/Reese/haltonseq.m

J. Montalt Tordera, shade.m, available at:
https://it.mathworks.com/matlabcentral/fileexchange/69652-filled-area-plot

R. Cavoretto, A. De Rossi, E. Perracchione, 
IntegerBased_MD_Structure, IntegerBased_MD_RangeSearch, 
IntegerBased_MD_Neighbourhood, IntegerBased_MD_ContainingQuery, 
available at: http://hdl.handle.net/2318/1559094

Required toolbox: Signal processing, optimization and
                  Statistics and Machine Learning Toolbox

Last update: 24/01/2022