Modified Metropolis-Hastings algorithm with Delayed Rejection

Modified Metropolis-Hastings algorithm with Delayed Rejection for High-Dimensional Reliability Analysis

The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in hig...

On Metropolis-Hastings algorithms with delayed rejection

Scaling analysis of delayed rejection MCMC methods

In this paper, we study the asymptotic efficiency of the delayed rejection strategy. In particular, the efficiency of the delayed rejection Metropolis-Hastings algorithm is compared to that of the regular Metropolis algorithm. To allow for a fair comparison, the study is carried under optimal mixing conditions for each of these algorithms. After introducing optimal scaling results for the delay...

R-package FME : MCMC tests

This vignette tests the Markov chain Monte Carlo (MCMC) implementation of Rpackage FME (Soetaert and Petzoldt 2010). It includes the delayed rejection and adaptive Metropolis algorithm (Haario, Laine, Mira, and Saksman 2006)

Monte Carlo Integration With Acceptance-Rejection

This article considers Monte Carlo integration under rejection sampling or Metropolis-Hastings sampling. Each algorithm involves accepting or rejecting observations from proposal distributions other than a target distribution. While taking a likelihood approach, we basically treat the sampling scheme as a random design, and define a stratified estimator of the baseline measure. We establish tha...