  We construct a parametrization of the deep-inelastic structure function of
the proton F_2 based on all available experimental information from charged
lepton deep-inelastic scattering experiments. The parametrization effectively
provides a bias-free determination of the probability measure in the space of
structure functions, which retains information on experimental errors and
correlations. The result is obtained in the form of a Monte Carlo sample of
neural networks trained on an ensemble of replicas of the experimental data. We
discuss in detail the techniques required for the construction of bias-free
parameterizations of large amounts of structure function data, in view of
future applications to the determination of parton distributions based on the
same method.
