Abstract:
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While Jeffreys priors usually are defined for the parameters of mixtures of distributions, they are not available in closed form. Furthermore, they often are improper priors. We study in this talk the implementation and the properties of Jeffreys priors in several mixture settings, show that the associated posterior distributions are most often improper, and then propose different non-informative alternatives for the analysis of location-scale mixtures.
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