Expected values and standard deviations are obtained by numerical
integration. The result is

It is interesting to show partial and global results
as contour lines at of the maximum of the
reweighting functions, equivalent to the
or
rules^{2}(I refer to Ref. [1] for the relation between
``standard'' methods based on minimization
and the more detailed inferential scheme illustrated there).
The top plot of Fig. 9 shows the contour
``roads''
given by the first three constraints, together with
the (almost) ellipse of their combination. The probability
that the values of and are
__both__ in the ellipse is about
37%.^{3}
Instead, the projections of the ellipse on each axis gives
an interval of about probability in __each__
variable.

The bottom plot of Fig. 9 shows the effect of the constraint . First we notice the perfect agreement between the and roads, indicating that the values of suggested by the data are absolutely consistent with the other constraints within the Standard Model. Furthermore, the effect of the on the ``ellipse'' of the final inference is to reshape the left side, increasing the value of and decreasing its uncertainty, with almost no effect on , as also shown by the results (16) and (17).