New Insights into History Matching via Sequential Monte Carlo

The aim of the history matching method is to locate non-implausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via a series of waves where at each wave an emulator is fitted to a small number of training samples. An implausibility measure is defined which takes into account the closeness of simulated and observed outputs as well as emulator uncertainty. As the waves progress, the emulator becomes more accurate so that training samples are more concentrated on promising regions of the space and poorer parts of the space are rejected with more confidence. Whilst history matching has proved to be useful, existing implementations are not fully automated and some ad-hoc choices are made during the process, which involves user intervention and is time consuming. This occurs especially when the non-implausible region becomes small and it is difficult to sample this space uniformly to generate new training points. In this article we develop a sequential Monte Carlo (SMC) algorithm for implementation which is semi-automated. Our novel SMC approach reveals that the history matching method yields a non-implausible distribution that can be multi-modal, highly irregular and very difficult to sample uniformly. Our SMC approach offers a much more reliable sampling of the non-implausible space, which requires additional computation compared to other approaches used in the literature

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.Comment: 5th IMS-ISBA joint meeting (MCMSki IV

An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions

The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss function over a, potentially, high-dimensional design space. A new general approach for approximately finding Bayesian optimal designs is proposed which uses computationally efficient normal-based approximations to posterior summaries to aid in approximating the expected loss. This new approach is demonstrated on illustrative, yet challenging, examples including hierarchical models for blocked experiments, and experimental aims of parameter estimation and model discrimination. Where possible, the results of the proposed methodology are compared, both in terms of performance and computing time, to results from using computationally more expensive, but potentially more accurate, Monte Carlo approximations. Moreover, the methodology is also applied to problems where the use of Monte Carlo approximations is computationally infeasible

Extending health messaging to the consumption experience: a focus group study exploring smokers’ perceptions of health warnings on cigarettes

Introduction: While most countries require health warnings on cigarette packs, the Scottish and Canadian Governments are considering requiring health warnings on cigarette sticks. Methods: Twenty focus groups were conducted in Glasgow and Edinburgh (Scotland) with smokers (n ¼ 120) segmented by age (16-17, 18-24, 25-35, 36-50, >50), gender and social grade, to explore perceptions of cigarettes displaying the warning 'Smoking kills' on the cigarette paper and any demographic differences in how smokers responded to these. Results: A warning on each cigarette was thought to prolong the health message, as it would be visible when a cigarette was taken from a pack, lit, left in an ashtray, and with each draw, and make avoi-dant behavior more difficult. That it would be visible to others was perceived as off-putting for some. It was felt that a warning on each cigarette would create a negative image and be embarrassing. Within several female groups they were viewed as depressing, worrying and frightening, with it suggested that people would not feel good smoking cigarettes displaying a warning. Within every group there was mention of warnings on cigarettes potentially having an impact on themselves, others or both. Some, mostly younger groups, mentioned stubbing cigarettes out early, reducing consumption or quitting. The consensus was that they would be off-putting for young people, nonsmokers and those starting to smoke. Conclusions: Including a warning on each cigarette stick is a viable policy option and one which would, for the first time, extend health messaging to the consumption experience

Optimal experimental design for predator–prey functional response experiments

Functional response models are important in understanding predator–prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs; these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle (Propylea quatuordecimpunctata L.) preying on the black bean aphid (Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications