Jeffreys priors for mixture estimation: properties and alternatives
Grazian, Clara; Robert, Christian P. (2018), Jeffreys priors for mixture estimation: properties and alternatives, Computational Statistics & Data Analysis, 121, p. 149-163. 10.1016/j.csda.2017.12.005
TypeArticle accepté pour publication ou publié
External document linkhttps://arxiv.org/abs/1511.03145
Journal nameComputational Statistics & Data Analysis
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Abstract (EN)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 non-informative alternative for the analysis of mixtures.
Subjects / KeywordsNoninformative prior; mixture of distributions; Bayesian analysis; Dirichlet prior; improper prior; improper posterior; labelswitching
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