STK_SAMPCRIT_EI_EVAL computes the EI criterion

 CALL: EI = stk_sampcrit_ei_eval (ZP_MEAN, ZP_STD, ZI)

    computes the value EI of the Expected Improvement (EI) criterion for a
    minimization problem, with respect to the observed values ZI, assuming
    Gaussian predictive distributions with means ZP_MEAN and standard
    deviations ZP_STD.  The input argument must have the following sizes:

       * ZP_MEAN    M x 1,
       * ZP_STD     M x 1,
       * ZI         N x 1,

    where M is the number of points where the EI must be computed, and N the
    number of observations.  The output has size M x 1.

 REMARK

    Since the EI is computed for a minimization problem, the result depends
    on the minimum of the obervations only, not on the entire set of observed
    values.  The above call is thus equivalent to

       EI = stk_sampcrit_ei_eval (ZP_MEAN, ZP_STD, min (ZI))

 NOTE

    This function was added in STK 2.4.1, and will in the future completely
    replace stk_distrib_normal_ei.  Note that, unlike the present function,
    stk_distrib_normal_ei returns as a default the EI for a *maximization*
    problem.

 REFERENCES

   [1] D. R. Jones, M. Schonlau and William J. Welch. Efficient global
       optimization of expensive black-box functions.  Journal of Global
       Optimization, 13(4):455-492, 1998.

   [2] J. Mockus, V. Tiesis and A. Zilinskas. The application of Bayesian
       methods for seeking the extremum. In L.C.W. Dixon and G.P. Szego,
       editors, Towards Global Optimization, volume 2, pages 117-129, North
       Holland, New York, 1978.