Главная

Chris Sherlock

  1. A hidden Markov model for disease interactions.

    Втр, 03/27/2012 - 15:01 — SomNambul
    Authors: Chris Sherlock, Tatiana Xifara, Sandra Telfer, Mike Begon
    Subjects: Applications
    Abstract

    To investigate interactions between parasite species in a host a population
    of field voles was studied longitudinally with presence or absence of six
    different parasites measured repeatedly.

  2. The Random Walk Metropolis: Linking Theory and Practice Through a Case Study.

    Втр, 11/30/2010 - 16:01 — SomNambul
    Authors: Chris Sherlock, Paul Fearnhead, Gareth O. Roberts
    Subjects: Methodology
    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).

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

    Пнд, 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.

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

    Пнд, 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.

Math-arch

RSS-материал