STK_EXAMPLE_MISC05  Parameter estimation for heteroscedastic noise variance

 DESCRIPTION

    We consider a 1d prediction problem with noisy data, where the variance of
    the noise depends on the input location.

    A simple heteroscedastic model is used, where the only parameter to be
    estimated is a dispersion parameter (the square of a scale parameter).
    More preciesely, the variance of the noise is assumed to be of the form

       tau^2(x) = phi * (x + 1) ^ 2,

    and the dispersion parameter phi is estimated together with the parameters
    of the covariance function.

 EXPERIMENTAL FEATURE WARNING

    This script demonstrates an experimental feature of STK (namely, gaussian
    noise model objects).  STK users that wish to experiment with it are
    welcome to do so, but should be aware that API-breaking changes are likely
    to happen in future releases.  We invite them to direct any questions,
    remarks or comments about this experimental feature to the STK mailing
    list.

 See also: stk_example_kb09