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Search Results: 1 - 10 of 8576 matches for " Chris Roberts "
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Enhancing Bayesian risk prediction for epidemics using contact tracing
Chris Jewell,Gareth Roberts
Quantitative Biology , 2012,
Abstract: Contact tracing data collected from disease outbreaks has received relatively little attention in the epidemic modelling literature because it is thought to be unreliable: infection sources might be wrongly attributed, or data might be missing due to resource contraints in the questionnaire exercise. Nevertheless, these data might provide a rich source of information on disease transmission rate. This paper presents novel methodology for combining contact tracing data with rate-based contact network data to improve posterior precision, and therefore predictive accuracy. We present an advancement in Bayesian inference for epidemics that assimilates these data, and is robust to partial contact tracing. Using a simulation study based on the British poultry industry, we show how the presence of contact tracing data improves posterior predictive accuracy, and can directly inform a more effective control strategy.
Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
Chris Sherlock,Gareth Roberts
Statistics , 2009, DOI: 10.3150/08-BEJ176
Abstract: Scaling of proposals for Metropolis algorithms is an important practical problem in MCMC implementation. Criteria for scaling based on empirical acceptance rates of algorithms have been found to work consistently well across a broad range of problems. Essentially, proposal jump sizes are increased when acceptance rates are high and decreased when rates are low. In recent years, considerable theoretical support has been given for rules of this type which work on the basis that acceptance rates around 0.234 should be preferred. This has been based on asymptotic results that approximate high dimensional algorithm trajectories by diffusions. In this paper, we develop a novel approach to understanding 0.234 which avoids the need for diffusion limits. We derive explicit formulae for algorithm efficiency and acceptance rates as functions of the scaling parameter. We apply these to the family of elliptically symmetric target densities, where further illuminating explicit results are possible. Under suitable conditions, we verify the 0.234 rule for a new class of target densities. Moreover, we can characterise cases where 0.234 fails to hold, either because the target density is too diffuse in a sense we make precise, or because the eccentricity of the target density is too severe, again in a sense we make precise. We provide numerical verifications of our results.
The Random Walk Metropolis: Linking Theory and Practice Through a Case Study
Chris Sherlock,Paul Fearnhead,Gareth O. Roberts
Statistics , 2010, DOI: 10.1214/10-STS327
Abstract: The random walk Metropolis (RWM) is one of the most common Markov chain Monte Carlo algorithms in practical use today. Its theoretical properties have been extensively explored for certain classes of target, and a number of results with important practical implications have been derived. This article draws together a selection of new and existing key results and concepts and describes their implications. The impact of each new idea on algorithm efficiency is demonstrated for the practical example of the Markov modulated Poisson process (MMPP). A reparameterization of the MMPP which leads to a highly efficient RWM-within-Gibbs algorithm in certain circumstances is also presented.
Flying-Fox Species Density - A Spatial Risk Factor for Hendra Virus Infection in Horses in Eastern Australia
Craig Smith, Chris Skelly, Nina Kung, Billie Roberts, Hume Field
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0099965
Abstract: Hendra virus causes sporadic but typically fatal infection in horses and humans in eastern Australia. Fruit-bats of the genus Pteropus (commonly known as flying-foxes) are the natural host of the virus, and the putative source of infection in horses; infected horses are the source of human infection. Effective treatment is lacking in both horses and humans, and notwithstanding the recent availability of a vaccine for horses, exposure risk mitigation remains an important infection control strategy. This study sought to inform risk mitigation by identifying spatial and environmental risk factors for equine infection using multiple analytical approaches to investigate the relationship between plausible variables and reported Hendra virus infection in horses. Spatial autocorrelation (Global Moran’s I) showed significant clustering of equine cases at a distance of 40 km, a distance consistent with the foraging ‘footprint’ of a flying-fox roost, suggesting the latter as a biologically plausible basis for the clustering. Getis-Ord Gi* analysis identified multiple equine infection hot spots along the eastern Australia coast from far north Queensland to central New South Wales, with the largest extending for nearly 300 km from southern Queensland to northern New South Wales. Geographically weighted regression (GWR) showed the density of P. alecto and P. conspicillatus to have the strongest positive correlation with equine case locations, suggesting these species are more likely a source of infection of Hendra virus for horses than P. poliocephalus or P. scapulatus. The density of horses, climate variables and vegetation variables were not found to be a significant risk factors, but the residuals from the GWR suggest that additional unidentified risk factors exist at the property level. Further investigations and comparisons between case and control properties are needed to identify these local risk factors.
The Ultraluminous State
J. C. Gladstone,T. P. Roberts,Chris Done
Physics , 2009, DOI: 10.1111/j.1365-2966.2009.15123.x
Abstract: (Abridged) We revisit the question of the nature of ULXs through a detailed investigation of their spectral shape, using the highest quality X-ray data available in the XMM-Newton public archives. We confirm that simple spectral models commonly used for the analysis and interpretation of ULXs (power-law continuum and multi-colour disc blackbody models) are inadequate in the face of such high quality data. Instead we find two near ubiquitous features in the spectrum: a soft excess and a roll-over in the spectrum at energies above 3keV. We investigate a range of more physical models to describe these data. We find that disc plus Comptonised corona models fit the data well, but the derived corona is cool, and optically thick (tau ~ 5-30). We argue that these observed disc temperatures are not a good indicator of the black hole mass as the powerful, optically thick corona drains energy from the inner disc, and obscures it. We estimate the intrinsic (corona-less) disc temperature, and demonstrate that in most cases it lies in the regime of stellar mass black holes. These objects have spectra which range from those similar to the highest mass accretion rate states in Galactic binaries, to those which clearly have two peaks, one at energies below 1 keV (from the outer, unComptonised disc) and one above 3 keV (from the Comptonised, inner disc). However, a few ULXs have a significantly cooler corrected disc temperature; we suggest that these are the most extreme stellar mass black hole accretors, in which a massive wind completely envelopes the inner disc regions, creating a cool photosphere. We conclude that ULXs provide us with an observational template for the transition between Eddington and super-Eddington accretion flows, with the latter occupying a new ultraluminous accretion state.
Irradiated, colour-temperature-corrected accretion discs in ultraluminous X-ray sources
Andrew D. Sutton,Chris Done,Timothy P. Roberts
Physics , 2014, DOI: 10.1093/mnras/stu1597
Abstract: Although attempts have been made to constrain the stellar types of optical counterparts to ULXs, the detection of optical variability instead suggests that they may be dominated by reprocessed emission from X-rays which irradiate the outer accretion disc. Here, we report results from a combined X-ray and optical spectral study of a sample of ULXs, which were selected for having broadened disc-like X-ray spectra, and known optical counterparts. We simultaneously fit optical and X-ray data from ULXs with a new spectral model of emission from an irradiated, colour-temperature-corrected accretion disc around a black hole, with a central Comptonising corona. We find that the ULXs require reprocessing fractions of $\sim 10^{-3}$, which is similar to sub-Eddington thermal dominant state BHBs, but less than has been reported for ULXs with soft ultraluminous X-ray spectra. We suggest that the reprocessing fraction may be due to the opposing effects of self-shielding in a geometrically thick super-critical accretion disc, and reflection from far above the central black hole by optically thin material ejected in a natal super-Eddington wind. Then, the higher reprocessing fractions reported for ULXs with wind-dominated X-ray spectra may be due to enhanced scattering onto the outer disc via the stronger wind in these objects. Alternatively, the accretion discs in these ULXs may not be particularly geometrically thick, rather they may be similar in this regard to the thermal dominant state BHBs.
The dynamics of simple gene network motifs subject to extrinsic fluctuations
Elijah Roberts,Shay Be'er,Chris Bohrer,Rati Sharma,Michael Assaf
Physics , 2015,
Abstract: Cellular processes do not follow deterministic rules, even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here we develop an analytical formalism that allows for calculation of the effect of extrinsic noise on gene expression motifs. We introduce a new method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a non-regulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene expression experiments.
Unlocking the nature of ultraluminous X-ray sources using their X-ray spectra
Jeanette C. Gladstone,Timothy P. Roberts,Chris Done
Physics , 2011, DOI: 10.1002/asna.201011496
Abstract: We explore the nature of ultraluminous X-ray sources through detailed investigations of their spectral shape using some of the highest quality data available in the XMM-Newton public archives. Phenomenological models allow us to characterise their spectra, while more 'physically-motivated' models enable us to explore the physical processes underlying these characteristics. These physical models imply the presence of extreme (probably super-Eddington) accretion on to stellar mass black holes.
Variational Inference for Gaussian Process Modulated Poisson Processes
Chris Lloyd,Tom Gunter,Michael A. Osborne,Stephen J. Roberts
Statistics , 2014,
Abstract: We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes. Such point processes are used in a variety of domains, including neuroscience, geo-statistics and astronomy, but their use is hindered by the computational cost of existing inference schemes. Our scheme: requires no discretisation of the domain; scales linearly in the number of observed events; and is many orders of magnitude faster than previous sampling based approaches. The resulting algorithm is shown to outperform standard methods on synthetic examples, coal mining disaster data and in the prediction of Malaria incidences in Kenya.
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Tom Gunter,Chris Lloyd,Michael A. Osborne,Stephen J. Roberts
Statistics , 2014,
Abstract: This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.
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