Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

@article{Bierkens2017PiecewiseDM,
  title={Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains},
  author={J. Bierkens and A. Bouchard-C{\^o}t{\'e} and A. Doucet and A. Duncan and P. Fearnhead and Thibaut Lienart and G. Roberts and S. Vollmer},
  journal={Statistics & Probability Letters},
  year={2017},
  volume={136},
  pages={148-154}
}
  • J. Bierkens, A. Bouchard-Côté, +5 authors S. Vollmer
  • Published 2017
  • Mathematics
  • Statistics & Probability Letters
  • Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain. (C) 2018 Elsevier B.V. All rights reserved. 
    35 Citations

    Figures from this paper

    Piecewise-Deterministic Markov Chain Monte Carlo
    • 56
    • Highly Influenced
    • PDF
    Reversible and non-reversible Markov chain Monte Carlo algorithms for reservoir simulation problems
    • 1
    • PDF
    Accelerating MCMC algorithms
    • 35
    • PDF
    Generalized Bouncy Particle Sampler
    • 20
    • PDF
    Coordinate sampler: a non-reversible Gibbs-like MCMC sampler
    • 9
    • Highly Influenced
    • PDF
    Reversible Jump PDMP Samplers for Variable Selection
    • 1
    • PDF
    On explicit $L^2$-convergence rate estimate for piecewise deterministic Markov processes
    • 1
    • PDF

    References

    SHOWING 1-10 OF 27 REFERENCES
    Bayesian Learning via Stochastic Gradient Langevin Dynamics
    • 1,163
    • PDF
    Limit theorems for the zig-zag process
    • 26
    • PDF
    Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
    • 237
    • PDF
    On Markov chain Monte Carlo methods for tall data
    • 156
    • PDF
    On Event-Chain Monte Carlo Methods
    • 6
    • PDF
    The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
    • 130
    • PDF