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Likelihood and Bayesian analysis of mixtures

Department of Statistics, University of Newcastle, Newcastle, UK, Education Statistics Services Center, Washington DC, USA, maitkin{at}air.org, irit.aitkin{at}ncl.ac.uk

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.

Key Words: Bayes • galaxy data • inference • Markov chain Monte Carlo • maximum likelihood • mixture model

Statistical Modelling, Vol. 1, No. 4, 287-304 (2001)
DOI: 10.1177/1471082X0100100404


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