  While Jeffreys priors usually are well-defined for the parameters of mixtures
of distributions, they are not available in closed form. Furthermore, they
often are improper priors. Hence, they have never been used to draw inference
on the mixture parameters. We study in this paper the implementation and the
properties of Jeffreys priors in several mixture settings, show that the
associated posterior distributions most often are improper, and then propose a
noninformative alternative for the analysis of mixtures.
