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A stable manifold MCMC method for high dimensions

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  • Beskos, Alexandros

Abstract

We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that their mixing time is robust to mesh refinement.

Suggested Citation

  • Beskos, Alexandros, 2014. "A stable manifold MCMC method for high dimensions," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 46-52.
    • Handle: RePEc:eee:stapro:v:90:y:2014:i:c:p:46-52
    DOI: 10.1016/j.spl.2014.03.016
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    References listed on IDEAS

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    1. Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013. "Advanced MCMC methods for sampling on diffusion pathspace," Stochastic Processes and their Applications, Elsevier, vol. 123(4), pages 1415-1453.
    2. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
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    Cited by:

    1. Matthew M. Graham & Alexandre H. Thiery & Alexandros Beskos, 2022. "Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1229-1256, September.

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