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...

introduction commuting between urban centers and rural areas is a phenomenon that observed in all countries with different social and economic structure. commuting is a new phenomenon in move of population that emerged with metropolis development. this phenomenon is different with other population movement. because this move does not change in residential place, population work in other place. ...

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...

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...

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...

The paper presents a new mutation strategy for the Metropolis light transport algorithm, which works in the space of uniform random numbers used to build up paths. Thus instead of mutating directly in the path space, mutations are realized in the infinite dimensional unit cube of pseudo-random numbers and these points are transformed to the path space according to BRDF sampling, light source sa...

Kobe City is a metropolis including large suburbs, where housing communities have been developed for many years. People have been recently moving to the urban areas and the suburbs are losing their power to attract population. At the same time, many blocks of high-rise flats have being built near railway stations adjoining town centers, and are drawing people of all generations. Residents with ...