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General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities

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  • Barone, Piero
  • Sebastiani, Giovanni
  • Stander, Julian

Abstract

We study general over-relaxation Markov chain Monte Carlo samplers for multivariate Gaussian densities. We provide conditions for convergence based on the spectral radius of the transition matrix and on detailed balance. We illustrate these algorithms using an image analysis example.

Suggested Citation

  • Barone, Piero & Sebastiani, Giovanni & Stander, Julian, 2001. "General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 115-124, April.
    • Handle: RePEc:eee:stapro:v:52:y:2001:i:2:p:115-124
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    References listed on IDEAS

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    1. G. O. Roberts & S. K. Sahu, 1997. "Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 291-317.
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