We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler. We develop conditions under which this sampler will be geometrically or uniformly ergodic. We apply our results to an algorithm for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon di...

This work presents a version of the Metropolis-Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis-Hastings sampling.

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...

We consider optimal temperature spacings for Metropolis-coupled Markov chain Monte Carlo (MCMCMC) and Simulated Tempering algorithms. We prove that, under certain conditions, it is optimal to space the temperatures so that the proportion of temperature swaps which are accepted is approximately 0.234. This generalises related work by physicists, and is consistent with previous work about optimal...

1 Research Center of Thalassemia and Hemoglobinopathy, Jundishapur University of Medical Sciences, Ahvaz, IR Iran 2 Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, IR Iran 3 Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands 4 Young Researchers Club, Science and Research Branch, Islamic Azad U...

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH). We develop conditions under which the CMH will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH used for drawing Bayesian inferences about the entire sample path of a diffusion process, base...

itself, in its progress to maturity. The whole system expands and strengthens, though all its pafrts are daily decaying and flying off" as effete materials, no longer necessary for the growing fabric. It is so with the community at large; it appears, and really is, in perpetual bloom and vigour of youth, while the individuals of which it is composed, are hourly mingling, in thousands, with thei...

1. Detailed criteria for priming and tolerance in the Metropolis searching algorithm. 2. Two-stage Metropolis search for parameter sets that exhibit priming or tolerance. 3. Statistical method used to identify backbone motifs. 4. Motif density is more robust than frequency to variation in the topological cut-off. 5. 2D parameter correlations demonstrate how parameter compensation affects topolo...

The Gibbs sampler, Metropolis’ algorithm, and similar iterative simulation methods are related to rejection sampling and importance sampling, two methods which have been traditionally thought of as non-iterative. We explore connections between importance sampling, iterative simulation, and importance-weighted resampling (SIR), and present new algorithms that combine aspects of importance sampli...

Correction to: Relationship between Religious Attitude and Prosocial Behavior Considering the Mediating Role of Empathy and Altruism in Nursing and Medical Students   Abdolzahra Naami1, Mahnaz Mehrabizadeh Honarmand1, Soodabeh Bassak Nejad2, Mahdi Hassanvand Amouzadeh3, Auob Asadi4 Nematollah Sanaeenasab4,   1 Professor, Department of Psychology, Faculty of Education and Psychology, Sh...