An investigation into the Multiple Optimised Parameter Estimation and Data compression algorithm

We investigate the use of the Multiple Optimised Parameter Estimation and Data compression algorithm (MOPED) for data compression and faster evaluation of likelihood functions. Since MOPED only guarantees maintaining the Fisher matrix of the likelihood at a chosen point, multimodal and some degenerate distributions will present a problem. We present examples of scenarios in which MOPED does faithfully represent the true likelihood but also cases in which it does not. Through these examples, we aim to define a set of criteria for which MOPED will accurately represent the likelihood and hence may be used to obtain a significant reduction in the time needed to calculate it. These criteria may involve the evaluation of the full likelihood function for comparison.Comment: 5 pages, 8 figures; corrections and additions to match version published in MNRAS Letters; added reference to published versio

Feasibility study for the value of pelvic floor distension in predicting mode of birth for women undergoing Vaginal Birth After Caesarean

Indroduction & hypothesis Women having Vaginal Birth (VB) have different soft tissue dynamics to women requiring emergency Lower Section Caesarean Section (LSCS). Aims To assess the role of ultrasound in the assessment of LH distensibility in predicting outcomes for women wishing for Vaginal Birth After Caesarean section (VBAC). To inform subsequent trial design including understanding womens attitudes to the use of ultrasound in prediction of vaginal birth Methods Nulliparous, previous VB and previous LSCS underwent a transvaginal ultrasound. This scan looked at the distensibility of the LH and then correlated with mode of birth. Analysis used logistic regression and ROC curves analysis for static measurements and distensibility. A second cohort was also asked about their views as to the usefulness of such a tool to help inform on the utility of such a model. Results The original hypothesis confirmed maternal BMI, Anterior Posterior (AP) diameter at rest and AP distensibility all being significant predictors of VB in nulliparous women. As expected this relationship was also seen in women who had previously had a vaginal birth. Of the VBAC group, 23 women had LSCS. Five were Robson category 18 had emergency LSCS in labour. 25 women had VB. Whilst there were trends towards lesser distensibility in VBAC women who delivered vaginally, none of these reached sgnificance. The concept of the use of scanning to inform women as to likelihood of successful vaginal birth was supported by the survey. Conclusion Previously noted characteristics in nulliparous women for pelvic floor distension were confirmed. This relationship was not demonstrated for the VBAC cohort. We were unable to establish criteria for a simple ultrasound model to predict VB in women wishing for VBAC. Overall, women would welcome such model if it were available

BAMBI: blind accelerated multimodal Bayesian inference

In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural network (NN) to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of a NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from Wilkinson Microwave Anisotropy Probe and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an even faster follow-up analysis using the same likelihood and different priors. This is a fully general algorithm that can be applied, without any pre-processing, to other problems with computationally expensive likelihood functions.Comment: 12 pages, 8 tables, 17 figures; accepted by MNRAS; v2 to reflect minor changes in published versio

Classifying LISA gravitational wave burst signals using Bayesian evidence

We consider the problem of characterisation of burst sources detected with the Laser Interferometer Space Antenna (LISA) using the multi-modal nested sampling algorithm, MultiNest. We use MultiNest as a tool to search for modelled bursts from cosmic string cusps, and compute the Bayesian evidence associated with the cosmic string model. As an alternative burst model, we consider sine-Gaussian burst signals, and show how the evidence ratio can be used to choose between these two alternatives. We present results from an application of MultiNest to the last round of the Mock LISA Data Challenge, in which we were able to successfully detect and characterise all three of the cosmic string burst sources present in the release data set. We also present results of independent trials and show that MultiNest can detect cosmic string signals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signals with SNR as low as ~8. In both cases, we show that the threshold at which the sources become detectable coincides with the SNR at which the evidence ratio begins to favour the correct model over the alternative.Comment: 21 pages, 11 figures, accepted by CQG; v2 has minor changes for consistency with accepted versio

Making a decision about surgery for female urinary incontinence: a qualitative study of women's views.

INTRODUCTION AND HYPOTHESIS: This qualitative interview study explores aspects women with urinary incontinence(UI) reflect upon when considering whether or not to have surgery. Conducted prior to the recent mesh pause in the UK, the article provides insights for current and future approaches to shared decision-making. METHODS: Qualitative in-depth interviews of 28 patients referred to secondary care for stress and mixed UI who were considering UI surgery. Participants were recruited from four urogynaecology clinics in the Midlands and South England, UK. Interviews were conducted in clinics, in patient homes, and by telephone. Data analysis was based on the constant comparative method. RESULTS: Participants' accounts comprised three key concerns: their experience of symptoms, the extent to which these impacted a variety of social roles and demands, and overcoming embarrassment. Accounts drew on individual circumstances, values, and concerns rather than objective or measurable criteria. In combination, these dimensions constituted a personal assessment of the severity of their UI and hence framed the extent to which women prioritized addressing their condition. CONCLUSIONS: Acknowledging women's personal accounts of UI shifts the concept of 'severity' beyond a medical definition to include what is important to patients themselves. Decision-making around elective surgery must endeavour to link medical information with women's own experiences and personal criteria, which often change in priority over time. We propose that this research provides insight into how the controversy around the use of mesh in the UK emerged. This study also suggests ways in which facilitating shared decision-making should be conducted in future