WORKSHOP
IN STATISTICAL MIXTURES AND LATENT-STRUCTURE MODELLING
International Centre
for Mathematical Sciences, Edinburgh, March 28 - March 30,
2000
Murray Aitkin, Department
of Statistics, University of Newcastle, UK and Education Statistics
Services Center, Washington DC, USA `Likelihood
and Bayesian analysis of mixtures'
The talk compares
likelihood and Bayesian analyses of finite mixture distributions, and expresses
reservations about the latter. In particular, the role of prior assumptions
in the full Monte Carlo Markov Chain Bayes analysis is obscure, yet these
assumptions clearly play a major role in the conclusions. These issues
are illustrated with a detailed discussion of the well-known galaxy data.
It is conjectured that differences in conclusions are due at least in part
to the use of the unequal-variance model and the prior distribution of
the component variances.
