Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to
approximate the posterior and derived quantities of interest. Despite this, the issue of how …

Abstract The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in
which the next state of the chain is selected among a set of proposals. We propose a …

In this article, we consider the implications of the fact that parallel raw-power can be
exploited by a generic Metropolis–Hastings algorithm if the proposed values are …

A general methodology is introduced for the construction and effective application of control
variates to estimation problems involving data from reversible Markov chain Monte Carlo …

Abstract Differential geometric Markov Chain Monte Carlo (MCMC) strategies exploit the
geometry of the target to achieve convergence in fewer MCMC iterations at the cost of …

Informed importance tempering (IIT) is an easy-to-implement MCMC algorithm that can be
seen as an extension of the familiar Metropolis-Hastings algorithm with the special feature …

We introduce a general framework that constructs estimators with reduced variance for
random walk Metropolis and Metropolis-adjusted Langevin algorithms. The resulting …

The waste-recycling Monte Carlo (WRMC) algorithm introduced by physicists is a
modification of the (multi-proposal) Metropolis–Hastings algorithm, which makes use of all …

Rao–Blackwellisation is a notion often occurring in the MCMC literature, with possibly
different meanings and connections with the original Rao–Blackwell theorem as established …

A classical approach for approximating expectations of functions wrt partially known
distributions is to compute the average of function values along a trajectory of a Metropolis …