  The problem of the estimation of distribution parameters in the case of
experimentally limited information is discussed. As an example the
determination of the total number of muons in Extensive Air Showers registered
in the KASCADE experiment is studied in details. Some methods based on other
than standard maximum-likelihood approach are examined. The advantages of the
new methods are shown. The comparison with the Artificial Neural Network
approach is also given.
