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On the Geometrical Convergence of Gibbs Sampler inRd

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  • Hwang, Chii-Ruey
  • Sheu, Shuenn-Jyi

Abstract

The geometrical convergence of the Gibbs sampler for simulating a probability distribution inRdis proved. The distribution has a density which is a bounded perturbation of a log-concave function and satisfies some growth conditions. The analysis is based on a representation of the Gibbs sampler and some powerful results from the theory of Harris recurrent Markov chains.

Suggested Citation

  • Hwang, Chii-Ruey & Sheu, Shuenn-Jyi, 1998. "On the Geometrical Convergence of Gibbs Sampler inRd," Journal of Multivariate Analysis, Elsevier, vol. 66(1), pages 22-37, July.
    • Handle: RePEc:eee:jmvana:v:66:y:1998:i:1:p:22-37
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    References listed on IDEAS

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    1. Amit, Y. & Grenander, U., 1991. "Comparing sweep strategies for stochastic relaxation," Journal of Multivariate Analysis, Elsevier, vol. 37(2), pages 197-222, May.
    2. Amit, Yali, 1991. "On rates of convergence of stochastic relaxation for Gaussian and non-Gaussian distributions," Journal of Multivariate Analysis, Elsevier, vol. 38(1), pages 82-99, July.
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    Cited by:

    1. Yin, G. & Zhang, Q. & Badowski, G., 2000. "Singularly Perturbed Markov Chains: Convergence and Aggregation," Journal of Multivariate Analysis, Elsevier, vol. 72(2), pages 208-229, February.

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