STK_EXAMPLE_KB01N  Ordinary kriging in 1D, with noisy data

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

 The Gaussian Process (GP) prior is the same as in stk_example_kb01.

 The observation noise is Gaussian and homoscedastic (constant variance).
 Its variance is assumed to be known.

 Note that the kriging predictor, which is the posterior mean of the GP,
 does NOT interpolate the data in this noisy example.

 See also: stk_example_kb01, stk_example_kb02n