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OctGPR is a package for using Gaussian Process Regression (GPR) in Octave.
GPR is a Bayesian statistical method of inference of unknown spatial data
from known samples. It is also know as Kriging in geostatistics field.
The method assumes that the known sample data are a result of a uniform spatial
Gaussian Process, with a constant or linear mean and constant variance.
Several models for the correlation function may be selected - gaussian,
exponential or inverse multiquadrics (more might be added in the future).
The mean (mu) and variance (var) parameters are ML-estimated analytically,
while the inverse spatial scales (theta) and white noise (nu) are ML-estimated
using a custom mixed-norm trust-region optimization algorithm. Derivatives
w.r.t. theta and *two* derivatives w.r.t. nu are calculated analytically,
hoping for a rapid convergence (as nu is typically the most sensitive
parameter).
In the future, the package will be extended with RBF models trained via GCV.
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