GETTING THE MEASURE OF THE FLATNESS PROBLEM

The problem of estimating cosmological parameters such as Omega from n oisy or incomplete data is an example of inverse problems and, as such , generally requires a probabilistic approach. We adopt the Bayesian i nterpretation of probability for such problems and stress the connecti on between probability and information which this approach makes expli cit. This connection is important even when information is 'minimal' o r, in other words, when we need to argue from a state of maximum ignor ance. We use the transformation group method of Jaynes to assign minim ally-informative prior probability measure for cosmological parameters in the simple example of a dust Friedmann model, showing that the usu al statements of the cosmological flatness problem are based on an ina ppropriate choice of prior. We further demonstrate that, in the framew ork of a classical cosmological model, there is no flatness problem.