The waste-recycling Monte Carlo (WR) algorithm, introduced by Frenkel, is a modification of the Metropolis-Hastings algorithm, which makes use of all the proposals, whereas the standard Metropolis-Hastings algorithm only uses the accepted proposals. We prove the convergence of the WR algorithm and its asymptotic normality. We give an example which shows that in general the WR algorithm is not a...

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 the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis–Hastings algorithm uses only the accepted proposals. In this paper we extend the WRMC algorithm to a general control variate technique and ex...

The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis-Hastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exh...

Necessary and su cient conditions for geometric con vergence in the relative supremum norm of the Metropolis Hastings simulation algorithm with a general generating function are estab lished An explicit expression for the convergence rate is given Introduction This paper discusses the convergence rate for the Metropolis Hastings simulation algorithm proposed in Hastings The Metropolis Hastings ...

The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) MetropolisHastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exhi...

This study considers using Metropolis-Hastings algorithm for stochastic simulation of chemical reactions. The proposed method uses SSA (Stochastic Simulation Algorithm) distribution which is a standard method for solving well-stirred chemically reacting systems as a desired distribution. A new numerical solvers based on exponential form of exact and approximate solutions of CME (Chemical Master...

This paper deals with the asymptotic properties of the Metropolis-Hastings algorithm, when the distribution of interest is unknown, but can be approximated by a sequential estimator of its density. We prove that, under very simple conditions, the rate of convergence of the Metropolis-Hastings algorithm is the same as that of the sequential estimator when the latter is introduced as the reversib...

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct...

The Hastings-Metropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental distribution (or proposal). We present an inhomogeneous Hastings-Metropolis algorithm for which the proposal density approximates the target density, as the number of iterations increa...

1.1 Dimension Changing The Metropolis-Hastings-Green algorithm (as opposed to just MetropolisHastings with no Green) is useful for simulating probability distributions that are a mixture of distributions having supports of different dimension. An early example (predating Green’s general formulation) was an MCMC algorithm for simulating spatial point processes (Geyer and Møller, 1994). More wide...