Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.Comment: 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6). Differences from first submission: some minor edits in response to feedback

Nested sampling for Potts models

Nested sampling is a new Monte Carlo method by Skilling [1] intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model

Effect of Ethanol on Microbial Community Structure and Function During Natural Attenuation of Benzene, Toluene, and \u3cem\u3eo\u3c/em\u3e-Xylene in a Sulfate-reducing Aquifer

Ethanol (EtOH) is a commonly used fuel oxygenate in reformulated gasoline and is an alternative fuel and fuel supplement. Effects of EtOH release on aquifer microbial ecology and geochemistry have not been well characterized in situ. We performed a controlled field release of petroleum constituents (benzene (B), toluene (T), o-xylene (o-X) at ∼1–3 mg/L each) with and without EtOH (∼500 mg/L). Mixed linear modeling (MLM) assessed effects on the microbial ecology of a naturally sulfidic aquifer and how the microbial community affected B, T, and o-X plume lengths and aquifer geochemistry. Changes in microbial community structure were determined by quantitative polymerase chain reaction (qPCR) targeting Bacteria, Archaea, and sulfate reducing bacteria (SRB); SRB were enumerated using a novel qPCR method targeting the adenosine-5′-phosphosulfate reductase gene. Bacterial and SRB densities increased with and without EtOH-amendment (1−8 orders of magnitude). Significant increases in Archaeal species richness; Archaeal cell densities (3–6 orders of magnitude); B, T, and o-X plume lengths; depletion of sulfate; and induction of methanogenic conditions were only observed with EtOH-amendment. MLM supported the conclusion that EtOH-amendment altered microbial community structure and function, which in turn lowered the aquifer redox state and led to a reduction in bioattenuation rates of B, T, and o-X

Delhi Flag Handover Ceremony 2010 volunteer project

The Delhi Flag Handover Ceremony (DFHC) was a project delivered by Glasgow Life on behalf of the Glasgow 2014 Organising Committee. The Handover Ceremony took place towards the end of the 2010 Commonwealth Games in Delhi and reflects the passing of responsibility for the Games from one host to the next (i.e. Glasgow). This report reflects on Glasgow’s approach to the DFHC, specifically its recruitment of a Mass Cast of 348 volunteers to participate in an 8-minute performance in Delhi. Glasgow sought to secure participation from across Scotland drawing on both semi-professional and amateur performers

Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to equip non-technical domain experts with a structured approach that will enable them to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses