on the Metropolis algorithm [@Metro].

The minimization is performed in several steps, increasing in each step the number of elements $h_{f,i}$ used for the determination of $h_f$. We start with a lattice consisting of only two points (the origin $x_p(0)=0$ and the position of the maximum $x_p(1)=x_{\max}$). This means that only one element $h_{f,1}$ is needed for the specification of $h_f$. The parameters to be fitted in this case are only two: $x_{\max}$ ($x_p(1)$) and the power $\alpha(1)$ determining the behaviour of $h_f(x)$ in the neighbourhood of the origin. The first step ends when the MC minimization has converged to some optimal values for the two fit-parameters. In the second step we use a lattice with three points $x_p(0),x_p(1),x_p(2)$ with $x_p(0)=0,~x_p(1)=\frac{x_{\max}}{2},~x_p(2)=x_{\max}$. Now we need two elements $h_{f,1}$ and $h_{f,2}$ to determine $h_f$. We do not keep the values of the old fit-parameters ($x_{\max}$,$\alpha(1)$) fixed in the second step. Instead we use Gaussian distributed random variables for the choice of the old fit-parameters. The mean values of these Gaussians are the optimum values obtained for these parameters in the previous step and the corresponding widths are taken small enough to allow only weak fluctuations ($\approx 10\%$) around the mean values. Again 