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Gareth Roberts

  1. Enhancing Bayesian risk prediction for epidemics using contact tracing.

    Fri, 03/16/2012 - 15:01 — SomNambul
    Authors: Gareth Roberts, Chris Jewell
    Subjects: Methodology
    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.

  2. Mcmc Methods for Functions: Modifying Old Algorithms to make them Faster.

    Mon, 02/06/2012 - 15:01 — SomNambul
    Authors: Gareth Roberts, Andrew Stuart, Simon Cotter, David White
    Subjects: Computation
    Abstract

    Many problems arising in applications result in the need to probe a
    probability distribution for functions. Examples include Bayesian nonparametric
    statistics and conditioned diffusion processes. Standard MCMC algorithms
    typically become arbitrarily slow under the mesh refinement dictated by
    nonparametric description of the unknown function. We describe an approach to
    modifying a whole range of MCMC methods which ensures that their speed of
    convergence is robust under mesh refinement.

  3. $\epsilon$-Strong Simulation of the Brownian Path.

    Tue, 10/04/2011 - 15:01 — SomNambul
    Authors: Gareth Roberts, Alexandros Beskos, Stefano Peluchetti
    Subjects: Computation
    Abstract

    We present an iterative sampling method which delivers upper and lower
    bounding processes for the Brownian path. We develop such processes with
    particular emphasis on being able to unbiasedly simulate them on a personal
    computer. The dominating processes converge almost surely in the supremum and
    $L_1$ norms.

  4. Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets.

    Mon, 09/07/2009 - 10:33 — SomNambul
    Authors: Chris Sherlock, Gareth Roberts
    Subjects: Computation
    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.

  5. Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets.

    Mon, 09/07/2009 - 10:33 — SomNambul
    Authors: Chris Sherlock, Gareth Roberts
    Subjects: Computation
    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.

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