There are other approaches to the objective determination of priors. In view of the above problems, a particularly attractive method for choosing a prior over a continuous model is proposed by Jeffreys (1961). The general idea of so-called Jeffreys priors is that the prior probability assigned to a small patch in the parameter space is proportional to, what may be called, the density of the distributions within that patch. Intuitively, if a lot of distributions, i.e., distributions that differ quite a lot among themselves, are packed together on a small patch in the parameter space, this patch should be given a larger prior probability than a similar patch within which there is little variation among the distributions (cf. Balasubramanian 2005). More technically, such a density is expressed by a prior distribution that is proportional to the Fisher information.  A key advantage of these priors is that they are invariant under reparameterizations of the parameter space: a new parameterization naturally leads to an adjusted density of distributions.