Statistical Modelling 1 (2001), 287304
Likelihood and Bayesian analysis of mixtures
Murray Aitkin
Department of Statistics, University of Newcastle, UK
and
Education Statistics Services Center, Washington DC, USA
email: maitkin@air.org
Abstract:
This paper 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.
Keywords:
Mixture model, maximum likelihood, Bayes, Markov Chain Monte Carlo,
inference, galaxy data.
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