Orateur
Dr
Kaniav Kamary
(Universite Paris-Dauphine / CEREMADE / INRIA, Saclay)
Description
While mixtures of Gaussian distributions have been studied for
more than a century, the construction of a reference Bayesian analysis
of those models remains unsolved, with a general prohibition of improper
priors due to the ill-posed nature of such statistical objects. This diculty
is usually bypassed by an empirical Bayes resolution . By creating a new
parameterisation centred on the mean and possibly the variance of the
mixture distribution itself, we manage to develop here a weakly informative
prior for a wide class of mixtures with an arbitrary number of components.
We demonstrate that some posterior distributions associated with this prior
and a minimal sample size are proper. We provide MCMC implementations
that exhibit the expected exchangeability.We only study here the univariate
case, the extension to multivariate location-scale mixtures being currently
under study. An R package called Ultimixt is associated with this paper.
Auteur principal
Dr
Kaniav Kamary
(Universite Paris-Dauphine / CEREMADE / INRIA, Saclay)
Co-auteurs
Dr
Christian P. Robert
(Universite Paris-Dauphine / University of Warwick)
Dr
Jeong Eun Lee
(Auckland University of Technology, New Zealand)
