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On population-based simulation for static inference

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Abstract

In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X n } n=1,…,N in parallel in order to simulate from some target density π (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156–163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653–666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411–436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731–792, 1997).

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Authors and Affiliations

  1. Department of Mathematics, Imperial College London, SW7 2AZ, London, UK

    Ajay Jasra

  2. Department of Mathematics and Statistics, McGill University, H3A 2K6, Montreal, Canada

    David A. Stephens

  3. Department of Statistics, University of Oxford, OX1 3TG, Oxford, UK

    Christopher C. Holmes

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  1. Ajay Jasra
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  2. David A. Stephens
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  3. Christopher C. Holmes
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Correspondence to Ajay Jasra.

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Jasra, A., Stephens, D.A. & Holmes, C.C. On population-based simulation for static inference. Stat Comput 17, 263–279 (2007). https://doi.org/10.1007/s11222-007-9028-9

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