  The problem of estimating cosmological parameters such as $\Omega$ from noisy
or incomplete data is an example of an inverse problem and, as such, generally
requires a probablistic approach. We adopt the Bayesian interpretation of
probability for such problems and stress the connection between probability and
information which this approach makes explicit.
  This connection is important even when information is ``minimal'' or, in
other words, when we need to argue from a state of maximum ignorance. We use
the transformation group method of Jaynes to assign minimally--informative
prior probability measure for cosmological parameters in the simple example of
a dust Friedman model, showing that the usual statements of the cosmological
flatness problem are based on an inappropriate choice of prior. We further
demonstrate that, in the framework of a classical cosmological model, there is
no flatness problem.
