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The problem of estimating cosmological parameters such as $\Omega$ from noisyor incomplete data is an example of an inverse problem and, as such, generallyrequires a probablistic approach. We adopt the Bayesian interpretation ofprobability for such problems and stress the connection between probability andinformation which this approach makes explicit.  This connection is important even when information is ``minimal'' or, inother words, when we need to argue from a state of maximum ignorance. We usethe transformation group method of Jaynes to assign minimally--informativeprior probability measure for cosmological parameters in the simple example ofa dust Friedman model, showing that the usual statements of the cosmologicalflatness problem are based on an inappropriate choice of prior. We furtherdemonstrate that, in the framework of a classical cosmological model, there isno flatness problem.Comment: 11 pages, submitted to Classical and Quantum Gravity, Tex source  file, no figur

Getting the measure of the flatness problem