STK_EXAMPLE_KB01  Ordinary kriging in 1D, with noiseless data

 This example shows how to compute ordinary kriging predictions on a
 one-dimensional noiseless dataset.

 The word 'ordinary' indicates that the mean function of the GP prior is
 assumed to be constant and unknown.

 A Matern covariance function is used for the Gaussian Process (GP) prior.
 The parameters of this covariance function are assumed to be known (i.e.,
 no parameter estimation is performed here).

 Note that the kriging predictor, which is the posterior mean of the GP,
 interpolates the data in this noiseless example.

 See also: stk_example_kb01n, stk_example_kb02