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1 results

  • 2 - Reversible MCMC and Its Scaling

  • Paul Fearnhead, Lancaster University, Christopher Nemeth, Newcastle University, Chris J. Oates, University of Newcastle upon Tyne, Chris Sherlock, Lancaster University
  • Book:
    Scalable Monte Carlo for Bayesian Learning
    Published online:
    16 May 2025
    Print publication:
    05 June 2025, pp 39-61

  • Summary

    This chapter delves into the theory and application of reversible Markov Chain Monte Carlo (MCMC) algorithms, focusing on their role in Bayesian inference. It begins with the Metropolis–Hastings algorithm and explores variations such as component-wise updates, and the Metropolis-Adjusted Langevin Algorithm (MALA). The chapter also discusses Hamiltonian Monte Carlo (HMC) and the importance of scaling MCMC methods for high-dimensional models or large datasets. Key challenges in applying reversible MCMC to large-scale problems are addressed, with a focus on computational efficiency and algorithmic adjustments to improve scalability.