the rycroft-malone paper states that co-production relies on ‘authentic’ collaboration as a context for action. our commentary supports and extends this assertion. we suggest that ‘authentic’ co-production involves processes where participants can ‘see’ the difference that they have made within the project and beyond. we provide examples including: the use of design in health projects which see...

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 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 paper gives geometric tools: comparison, Nash and Sobolev inequalities for pieces of the relevent Markov operators, that give useful bounds on rates of convergence for the Metropolis algorithm. As an example, we treat the random placement of N hard discs in the unit square, the original application of the Metropolis algorithm.

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 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...

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...