  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. The implementation and the properties of Jeffreys
priors in several mixture settings are studied. It is shown that the associated
posterior distributions most often are improper. Nevertheless, the Jeffreys
prior for the mixture weights conditionally on the parameters of the mixture
components will be shown to have the property of conservativeness with respect
to the number of components, in case of overfitted mixture and it can be
therefore used as a default priors in this context.
