Markov chain Monte Carlo methods (MCMC) are essential tools for solving many modern-
day statistical and computational problems; however, a major limitation is the inherently …

We consider optimal temperature spacings for Metropolis-coupled Markov chain Monte
Carlo (MCMCMC) and Simulated Tempering algorithms. We prove that, under certain …

In this work, we introduce an innovative Markov Chain Monte Carlo (MCMC) classifier, a
synergistic combination of Bayesian machine learning and Apache Spark, highlighting the …

Abstract Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of
proposals at each step to efficiently resolve a target probability distribution. We build a …

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 …

The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo
algorithm is critical to the quality of the generated samples. Much work has gone into …

We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that
generate multiple proposals at each step. This strategy makes the rate-limiting step within …

Abstract Casella and Robert [Biometrika 83 (1996) 81–94] presented a general Rao–
Blackwellization principle for accept-reject and Metropolis–Hastings schemes that leads to …

Section A of the supplementary material provides additional intuition for the algorithms
discussed in this work (in addition to the works cited above, this includes a link with …

Approximate Bayesian computation enables inference for complicated probabilistic models
with intractable likelihoods using model simulations. The Markov chain Monte Carlo …