Automatic Zig-Zag sampling in practice

Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms based on Piecewise Deterministic Markov Processes (PDMPs), non-reversible continuous-time processes, are developing into their own research branch, thanks their important properties (e.g., correct invariant distribution, ergodicity, and super-efficiency). Nevertheless, practice has not caught up with the theory in this field, and the use of PDMPs to solve applied problems is not widespread. This might be due, firstly, to several implementational challenges that PDMP-based samplers present with and, secondly, to the lack of papers that showcase the methods and implementations in applied settings. Here, we address both these issues using one of the most promising PDMPs, the Zig-Zag sampler, as an archetypal example. After an explanation of the key elements of the Zig-Zag sampler, its implementation challenges are exposed and addressed. Specifically, the formulation of an algorithm that draws samples from a target distribution of interest is provided. Notably, the only requirement of the algorithm is a closed-form function to evaluate the target density of interest, and, unlike previous implementations, no further information on the target is needed. The performance of the algorithm is evaluated against another gradient-based sampler, and it is proven to be competitive, in simulation and real-data settings. Lastly, we demonstrate that the super-efficiency property, i.e. the ability to draw one independent sample at a lesser cost than evaluating the likelihood of all the data, can be obtained in practice.Comment: Small edits from previous version following some minor revisions requeste

Above‐ and below‐ground assessment of carabid community responses to crop type and tillage

Carabid beetles are major predators in agro‐ecosystems. The composition of their communities within crop environments governs the pest control services they provide. An understudied aspect is the distribution of predacious carabid larvae in the soil. We used novel subterranean trapping with standard pitfall trapping, within a multi‐crop rotation experiment, to assess the responses of above‐ and below‐ground carabid communities to management practices. Crop and trap type significantly affected pooled carabid abundance with an interaction of the two, the highest numbers of carabids were caught in subterranean traps in barley under sown with grass. Trap type accounted for the most variance observed in carabid community composition, followed by crop. Tillage responses were only apparent at the species level for three of the eight species modelled. Responses to crop type varied by species. Most species had higher abundance in under‐sown barley, than grass, wheat and barley. Crop differences were greater in the subterranean trap data. For predaceous larvae, standard pitfalls showed lowest abundances in under‐sown barley, yet subterranean traps revealed abundances to be highest in this crop. Comprehensive estimation of ecosystem services should incorporate both above‐ and below‐ground community appraisal, to inform appropriate management

Species matter when considering landscape effects on carabid distributions

Increasing the abundance and diversity of carabid beetles is a common objective of farm habitat management to deliver sustainable pest control. Carabid spatial distributions in relation to crop areas are important to the delivery of this ecosystem service. We used pitfall count data at distances from edge habitats into crop centres, from farm sites across the UK, to determine the effects of in-field and adjacent environmental features on carabid abundance and diversity. Overall carabid abundance increased towards the crop centre, whilst species richness and diversity decreased. The analyses of carabid abundance based on all the species pooled together strongly reflected the behaviour of the most abundant species. Species preferences varied by crop, soil type, and environmental features. For instance, some species were positively associated with habitats such as margins, while others responded negatively. This contrast in individual species models highlights the limitations on pooled models in elucidating responses. Studies informing farm-habitat design should consider individual species’ preferences for effective enhancement of pest control services. Diverse cropping and landscape heterogeneity at the farm scale can benefit the varied preferences of individual species, help build diverse communities and, potentially increase service resilience and stability over time

Gambling problems and the impact of family in UK armed forces veterans

Background and aims International evidence indicates elevated problem gambling rates in armed forces veterans compared with the general population. Gambling problems adversely impact one’s family, and family-related variables may increase vulnerability to gambling-related harm. Little is known, however, about gambling problems in the United Kingdom (UK) veterans or to what extent family variables, such as parenting history and experience of domestic violence, influence veterans’ gambling. Methods We compared veterans (n = 257) and sex- and age-matched controls (n = 514) drawn from the 2007 Adult Psychiatric Morbidity Survey on gambling, financial management, domestic violence, childhood parental presence, and experience of stressful life events. Veterans who left the military before or after 4 years of service were compared. Results Problem gambling was significantly more prevalent in veterans (1.4%) than non-veterans (0.2%), and the impact of gambling problems on the family was specific to male veterans, particularly those who had experienced a traumatic event after the age of 16, and those who were more likely to have been physically attacked by their partner. Overall, this study revealed that the UK armed forces veterans report a higher prevalence rate of problem gambling compared with non-veterans, with potential negative impact on family life

Quantile regression analysis of in-play betting in a large online gambling dataset

In-play betting involves making multiple bets during a sporting event and is an increasingly popular form of gambling. Behavioural analysis of large datasets of in-play betting may aid in the prediction of at-risk patterns of gambling. However, datasets may contain significant skew and outliers necessitating analytical approaches capable of examining behaviour across the spectrum of involvement with in-play betting. Here, we employ quantile regression analyses to investigate the relationships between in-play betting behaviours of frequency and duration of play, bets per day, net/percentage change, average stake, and average/percentage change across groups of users differing by betting involvement. The dataset consisted of 24,781 in-play sports bettors enrolled with an internet sports betting provider in February 2005. We examined trends in normally-involved and heavily-involved in-play bettor groups at the .1, .3, .5, .7 and .9 quantiles. The relationship between the total number of in-play bets and the remaining in-play betting measures was dependent on degree of involvement. The only variable to differ from this analytic path was the standard deviation in the daily average stake for most-involved bettors. The direction of some relationships, such as the frequency of play and bets per betting day, were reversed for most-involved bettors. Crucially, this highlights the importance of determining how these relationships vary across the spectrum of involvement with in-play betting. In conclusion, quantile regression provides a comprehensive account of the relationship between in-play betting behaviours capable of quantifying changes in magnitude and direction that vary by involvement

Action 3:30R: Results of a cluster randomised feasibility study of a revised teaching assistant-led extracurricular physical activity intervention for 8 to 10 year olds

Many children are not sufficiently physically active. We conducted a cluster-randomised feasibility trial of a revised after-school physical activity (PA) programme delivered by trained teaching assistants (TAs) to assess the potential evidence of promise for increasing moderate-to-vigorous physical activity (MVPA). Participants (n = 335) aged 8–10 years were recruited from 12 primary schools in South West England. Six schools were randomised to receive the intervention and six acted as non-intervention controls. In intervention schools, TAs were trained to deliver an after-school programme for 15 weeks. The difference in mean accelerometer-assessed MVPA between intervention and control schools was assessed at follow-up (T1). The cost of programme delivery was estimated. Two schools did not deliver the intervention, meaning four intervention and six control schools were analysed at T1. There was no evidence for a difference in MVPA at T1 between intervention and control groups. Programme delivery cost was estimated at £2.06 per pupil per session. Existing provision in the 12 schools cost £5.91 per pupil per session. Action 3:30 was feasible to deliver and considerably cheaper than existing after-school provision. No difference in weekday MVPA was observed at T1 between the two groups, thus progression to a full trial is not warranted

Ovarian cancer symptom awareness and anticipated delayed presentation in a population sample

Background: While ovarian cancer is recognised as having identifiable early symptoms, understanding of the key determinants of symptom awareness and early presentation is limited. A population-based survey of ovarian cancer awareness and anticipated delayed presentation with symptoms was conducted as part of the International Cancer Benchmarking Partnership (ICBP). Methods: Women aged over 50 years were recruited using random probability sampling (n = 1043). Computer-assisted telephone interviews were used to administer measures including ovarian cancer symptom recognition, anticipated time to presentation with ovarian symptoms, health beliefs (perceived risk, perceived benefits/barriers to early presentation, confidence in symptom detection, ovarian cancer worry), and demographic variables. Logistic regression analysis was used to identify the contribution of independent variables to anticipated presentation (categorised as < 3 weeks or ≥ 3 weeks). Results: The most well-recognised symptoms of ovarian cancer were post-menopausal bleeding (87.4%), and persistent pelvic (79.0%) and abdominal (85.0%) pain. Symptoms associated with eating difficulties and changes in bladder/bowel habits were recognised by less than half the sample. Lower symptom awareness was significantly associated with older age (p ≤ 0.001), being single (p ≤ 0.001), lower education (p ≤ 0.01), and lack of personal experience of ovarian cancer (p ≤ 0.01). The odds of anticipating a delay in time to presentation of ≥ 3 weeks were significantly increased in women educated to degree level (OR = 2.64, 95% CI 1.61 – 4.33, p ≤ 0.001), women who reported more practical barriers (OR = 1.60, 95% CI 1.34 – 1.91, p ≤ 0.001) and more emotional barriers (OR = 1.21, 95% CI 1.06 – 1.40, p ≤ 0.01), and those less confident in symptom detection (OR = 0.56, 95% CI 0.42 – 0.73, p ≤ 0.001), but not in those who reported lower symptom awareness (OR = 0.99, 95% CI 0.91 – 1.07, p = 0.74). Conclusions: Many symptoms of ovarian cancer are not well-recognised by women in the general population. Evidence-based interventions are needed not only to improve public awareness but also to overcome the barriers to recognising and acting on ovarian symptoms, if delays in presentation are to be minimised