Accelerating MCMC algorithms

Christian P. Robert*, Víctor Elvira, Nick Tawn, Changye Wu

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract / Description of output

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao–Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Algorithms and Computational Methods > Algorithms Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods.

Original languageEnglish
Article numbere1435
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number5
Early online date13 Jun 2018
Publication statusPublished - 1 Sept 2018

Keywords / Materials (for Non-textual outputs)

  • Bayesian analysis
  • computational statistics
  • convergence of algorithms
  • efficiency of algorithms
  • Hamiltonian Monte Carlo
  • Monte Carlo methods
  • Rao-Blackwellisation
  • simulation
  • tempering


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