Statistical Modelling 1 (2001), 287–304

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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