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On rates of convergence for posterior distributions in infinite–dimensional models

Author

Listed:
  • Antonio Lijoi
  • Igor Prünster
  • Stephen G. Walker

Abstract

This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. Crucially, no sieve or entropy measures are required and so rates do not depend on the rate of convergence of the corresponding sieve maximum likelihood estimator. In particular, we improve on current rates for mixture models.

Suggested Citation

  • Antonio Lijoi & Igor Prünster & Stephen G. Walker, 2004. "On rates of convergence for posterior distributions in infinite–dimensional models," ICER Working Papers - Applied Mathematics Series 24-2004, ICER - International Centre for Economic Research.
    • Handle: RePEc:icr:wpmath:24-2004
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    File URL: http://www.bemservizi.unito.it/repec/icr/wp2004/Pruenster24-04.pdf
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    References listed on IDEAS

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    Keywords

    Hellinger consistency; mixture of Dirichlet process; posterior distribution; rates of convergence;
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