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Paul Fearnhead

  1. Markov chain Monte Carlo for exact inference for diffusions.

    Tue, 03/01/2011 - 16:01 — SomNambul
    Authors: Paul Fearnhead, Omiros Papaspiliopoulos, Gareth O. Roberts, Giorgos Sermaidis, Alex Beskos
    Subjects: Methodology
    Abstract

    We develop exact Markov chain Monte Carlo methods for discretely-sampled,
    directly and indirectly observed diffusions. The qualification "exact" refers
    to the fact that the invariant and limiting distribution of the Markov chains
    is the exact posterior distribution of the parameters of interest. The class of
    processes to which our methods directly apply are those which can be simulated
    using the most general to date exact simulation algorithm. The article
    introduces various methods to boost the performance of the basic scheme,
    including reparametrizations and auxiliary Poisson sampling.

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

    Tue, 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. On Estimating the Ability of NBA Players.

    Thu, 08/05/2010 - 15:01 — SomNambul
    Authors: Paul Fearnhead, Benjamin M. Taylor
    Subjects: Applications
    Abstract

    This paper introduces a new model and methodology for estimating the ability
    of NBA players. The main idea is to directly measure how good a player is by
    comparing how their team performs when they are on the court as opposed to when
    they are off it. This is achieved in a such a way as to control for the
    changing abilities of the other players on court at different times during a
    match.

  4. An Adaptive Sequential Monte Carlo Sampler.

    Mon, 05/10/2010 - 15:01 — SomNambul
    Authors: Paul Fearnhead, Benjamin M. Taylor
    Subjects: Computation
    Abstract

    Sequential Monte Carlo (SMC) methods are not only a popular tool in the
    analysis of state{space models, but o?er an alternative to MCMC in situations
    where Bayesian inference must proceed via simulation. This paper introduces a
    new SMC method that uses adaptive MCMC kernels for particle dynamics. The
    proposed algorithm features an online stochastic optimization procedure to
    select the best MCMC kernel and simultaneously learn optimal tuning parameters.
    Theoretical results are presented that justify the approach and give guidance
    on how it should be implemented.

  5. Semi-automatic Approximate Bayesian Computation.

    Thu, 04/08/2010 - 15:01 — SomNambul
    Authors: Paul Fearnhead, Dennis Prangle
    Subjects: Methodology
    Abstract

    Many modern statistical applications involve inference for complex stochastic
    models, where it is easy to simulate from the models, but impossible to
    calculate likelihoods. Approximate Bayesian Computation (ABC) is a method of
    inference for such models. It replaces calculation of the likelihood by a step
    which involves simulating artificial data for different parameter values, and
    comparing summary statistics of the simulated data to summary statistics of the
    observed data. Here we show how to construct appropriate summary statistics for
    ABC in a semi-automatic manner.

  6. Efficient Bayesian analysis of multiple changepoint models with dependence across segments.

    Mon, 10/19/2009 - 13:48 — SomNambul
    Authors: Paul Fearnhead, Zhen Liu
    Subjects: Computation
    Abstract

    We consider Bayesian analysis of a class of multiple changepoint models.
    While there are a variety of efficient ways to analyse these models if the
    parameters associated with each segment are independent, there are few general
    approaches for models where the parameters are dependent. Under the assumption
    that the dependence is Markov, we propose an efficient online algorithm for
    sampling from an approximation to the posterior distribution of the number and
    position of the changepoints. In a simulation study, we show that the
    approximation introduced is negligible.

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