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Stability of Partially Implicit Langevin Schemes and Their MCMC Variants

Author

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  • Casella, Bruno
  • Roberts, Gareth O.
  • Stramer, Osnat
Abstract
A broad class of implicit or partially implicit time discretizations for the Langevin diffusion are considered and used as proposals for the Metropolis–Hastings algorithm. Ergodic properties of our proposed schemes are studied. We show that introducing implicitness in the discretization leads to a process that often inherits the convergence rate of the continuous time process. These contrast with the behavior of the naive or Euler–Maruyama discretization, which can behave badly even in simple cases. We also show that our proposed chains, when used as proposals for the Metropolis–Hastings algorithm, preserve geometric ergodicity of their implicit Langevin schemes and thus behave better than the local linearization of the Langevin diffusion. We illustrate the behavior of our proposed schemes with examples. Our results are described in detail in one dimension only, although extensions to higher dimensions are also described and illustrated.

Suggested Citation

  • Casella, Bruno & Roberts, Gareth O. & Stramer, Osnat, 2011. "Stability of Partially Implicit Langevin Schemes and Their MCMC Variants," MPRA Paper 95220, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:95220
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    File URL: https://mpra.ub.uni-muenchen.de/95220/1/MPRA_paper_95220.pdf
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    References listed on IDEAS

    as
    1. Mattingly, J. C. & Stuart, A. M. & Higham, D. J., 2002. "Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise," Stochastic Processes and their Applications, Elsevier, vol. 101(2), pages 185-232, October.
    2. O. Stramer & R. L. Tweedie, 1999. "Langevin-Type Models II: Self-Targeting Candidates for MCMC Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 1(3), pages 307-328, October.
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    More about this item

    Keywords

    Langevin diffusions Ergodicity Implicit Euler schemes: discrete approximation;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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