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Sumeetpal S. Singh

  1. Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation".

    Mon, 01/09/2012 - 15:01 — SomNambul
    Authors: Arnaud Doucet, Sumeetpal S. Singh, Christian P. Robert, Nicolas Chopin, Jean-Michel Marin, Julien Cornebise, Ioannis Kosmidis, Christophe Andrieu, Pierre Pudlo, Ajay Jasra, Anthony Lee, Simon Barthelme, Mark Girolami, Mohammed Sedki.
    Subjects: Methodology
    Abstract

    This report is a collection of comments on the Read Paper of Fearnhead and
    Prangle (2011), to appear in the Journal of the Royal Statistical Society
    Series B, along with a reply from the authors.

  2. Asymptotic Behaviour of Approximate Bayesian Estimators.

    Thu, 05/19/2011 - 15:01 — SomNambul
    Authors: Sumeetpal S. Singh, Thomas A. Dean
    Subjects: Statistics
    Abstract

    Although approximate Bayesian computation (ABC) has become a popular
    technique for performing parameter estimation when the likelihood functions are
    analytically intractable there has not as yet been a complete investigation of
    the theoretical properties of the resulting estimators. In this paper we give a
    theoretical analysis of the asymptotic properties of ABC based parameter
    estimators for hidden Markov models and show that ABC based estimators satisfy
    asymptotically biased versions of the standard results in the statistical
    literature.

  3. Parameter Estimation for Hidden Markov Models with Intractable Likelihoods.

    Tue, 03/29/2011 - 15:01 — SomNambul
    Authors: Sumeetpal S. Singh, Gareth W. Peters, Ajay Jasra, Thomas A. Dean
    Subjects: Statistics
    Abstract

    Approximate Bayesian computation (ABC) is a popular technique for
    approximating likelihoods and is often used in parameter estimation when the
    likelihood functions are analytically intractable. Although the use of ABC is
    widespread in many fields, there has been little investigation of the
    theoretical properties of the resulting estimators. In this paper we give a
    theoretical analysis of the asymptotic properties of ABC based maximum
    likelihood parameter estimation for hidden Markov models.

  4. A Backward Particle Interpretation of Feynman-Kac Formulae.

    Wed, 08/19/2009 - 19:30 — SomNambul
    Authors: Pierre Del Moral, Arnaud Doucet, Sumeetpal S. Singh
    Subjects: gr. Statistics
    Abstract

    We design a particle interpretation of Feynman-Kac measures on path spaces
    based on a backward Markovian representation combined with a traditional mean
    field particle interpretation of the flow of their final time marginals. In
    contrast to traditional genealogical tree based models, these new particle
    algorithms can be used to compute normalized additive functionals "on-the-fly"
    as well as their limiting occupation measures with a given precision degree
    that does not depend on the final time horizon.

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