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On rates of convergence of stochastic relaxation for Gaussian and non-Gaussian distributions

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  • Amit, Yali

Abstract

We obtain rates of convergence of stochastic relaxation (heat bath algorithm) for continuous densities which have the form of bounded perturbations of Gaussian densities. The rates are calculated in the spaces of square integrable functions with respect to these desities in which the operator generated by the stochastic relaxation process has the form of a product of projections.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:38:y:1991:i:1:p:82-99
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    Cited by:

    1. 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.
    2. Marcin Mider & Paul A. Jenkins & Murray Pollock & Gareth O. Roberts, 2022. "The Computational Cost of Blocking for Sampling Discretely Observed Diffusions," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 3007-3027, December.
    3. Roberto Leon-Gonzalez, "undated". "Data Augmentation in Limited-Dependent Variable Models," Discussion Papers 02/09, Department of Economics, University of York.
    4. Yuen, Wai Kong, 2000. "Applications of geometric bounds to the convergence rate of Markov chains on," Stochastic Processes and their Applications, Elsevier, vol. 87(1), pages 1-23, May.
    5. Rosenthal, Jeffrey S., 1996. "Markov chain convergence: From finite to infinite," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 55-72, March.

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