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Geometric convergence of the Metropolis-Hastings simulation algorithm

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  • Holden, Lars

Abstract

Necessary and sufficient conditions for uniform geometric convergence in the relative supremum norm of the Metropolis-Hastings simulation algorithm with a general generating function are established. An explicit expression for the convergence rate is given.

Suggested Citation

  • Holden, Lars, 1998. "Geometric convergence of the Metropolis-Hastings simulation algorithm," Statistics & Probability Letters, Elsevier, vol. 39(4), pages 371-377, August.
  • Handle: RePEc:eee:stapro:v:39:y:1998:i:4:p:371-377
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    Cited by:

    1. Altaleb, Anas & Chauveau, Didier, 2002. "Bayesian analysis of the Logit model and comparison of two Metropolis-Hastings strategies," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 137-152, April.
    2. Fabian Böhm & Diego Alonso-Urquijo & Guy Verschaffelt & Guy Van der Sande, 2022. "Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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