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 > AlgorithmsStatistical and Graphical Methods of Data Analysis > Monte Carlo Methods.
Keywords: Bayesian analysis; Hamiltonian Monte Carlo; Monte Carlo methods; Rao‐Blackwellisation; computational statistics; convergence of algorithms; efficiency of algorithms; simulation; tempering.