Multi-target out-of-sequence data association

In data fusion systems, one often encounters measurements of past target locations and then wishes to deduce where the targets are currently located. Recent research on the processing of such out-of-sequence data has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships between the algorithms so that any approximations made are explicit

Tackling threats to informed decision-making in democratic societies: Promoting epistemic security in a technologically-advanced world

Access to reliable information is crucial to the ability of a democratic society to coordinate effective collective action when responding to a crisis, like a global pandemic, or complex challenge like climate change. Through a series of workshops we developed and analysed a set of hypothetical yet plausible crisis scenarios to explore how technologically exacerbated external threats and internal vulnerabilities to a society’s epistemic security – its ability to reliably avert threats to the processes by which reliable information is produced, distributed, and assessed within the society – can be mitigated in order to facilitate timely decision-making and collective action in democratic societies. Overall we observed that preserving a democratic society’s epistemic security is a complex effort that sits at the interface of many knowledge domains, theoretical perspectives, value systems, and institutional responsibilities, and we developed a series of recommendations to highlight areas where additional research and resources will likely have a significant impact on improving epistemic security in democratic societie

Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag

Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for "Pillar 2" swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation now-casting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo

Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag

Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for ‘Pillar 2’ swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo

The thin(ning) green line? Investigating changes in Kenya's seagrass coverage

Knowledge of seagrass distribution is limited to a few well-studied sites and poor where resourcesare scant (e.g. Africa), hence global estimates of seagrass carbon storage are inaccurate. Here, we analysed freely available Sentinel-2 and Landsat imagery to quantify contemporary coverage and change in seagrass between 1986 and 2016 on Kenya’s coast. Using field surveys and independent estimates of historical seagrass, we estimate total cover of Kenya’s seagrass to be 317.1 ± 27.2 km226 , following losses of 0.85% yr-1 since 1986. Losses increased from 0.29% yr-1 in 2000 to 1.59% yr-1 in 2016, releasing up to 2.17 Tg carbon since 1986. Anecdotal evidence suggests fishing pressure is an important cause of loss and is likely to intensify in the near future. If these results are representative for Africa, global estimates of seagrass extent and loss need reconsidering

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201