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Perfect posterior simulation for mixture and hidden Markov models

Part of: Markov processes Parametric inference

Published online by Cambridge University Press:  01 August 2010

Kasper K. Berthelsen ,
Laird A. Breyer  and
Gareth O. Roberts
Kasper K. Berthelsen
Affiliation:
Department of Mathematical Sciences, Aalborg University, 9220 Aalborg Øst, Denmark (email: kkb@math.aau.dk)
Laird A. Breyer
Affiliation:
Department of Statistics, Lancaster University, Bailrigg Lancaster LA1 4YF, United Kingdomhttp://www.lbreyer.com/
Gareth O. Roberts
Affiliation:
Department of Statistics, University of Warwick, Coventry CV4 7AL, United Kingdom (email: gareth.o.roberts@warwick.ac.uk)

Abstract

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In this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models. We describe a method for perfect simulation from the posterior distribution of the unknown mixture weights in a mixture model. Our method is extended to a more general mixture problem, where unknown parameters exist for the mixture components, and to a hidden Markov model.

MSC classification

Secondary: 60J22: Computational methods in Markov chains 62F12: Asymptotic properties of estimators
Type
Research Article
Information
LMS Journal of Computation and Mathematics , Volume 13 , January 2010 , pp. 246 - 259
Copyright
Copyright © London Mathematical Society 2010

References

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