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Free Energy Sequential Monte Carlo Application to Mixture Modelling

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  • Nicolas Chopin

    (Crest)

  • Pierre Jacob

    (Crest)

Abstract

We introduce a new class of Sequential Monte Carlo (SMC) methods, whichwe call free energy SMC. This class is inspired by free energy methods, whichoriginate from Physics, and where one samples from a biased distribution suchthat a given function !(") of the state " is forced to be uniformly distributedover a given interval. From an initial sequence of distributions (#t) of interest,and a particular choice of !("), a free energy SMC sampler computes sequentiallya sequence of biased distributions (˜#t) with the following properties: (a)the marginal distribution of !(") with respect to ˜#t is approximatively uniformover a specified interval, and (b) ˜#t and #t have the same conditional distributionwith respect to !. We apply our methodology to mixture posteriordistributions, which are highly multimodal. In the mixture context, forcingcertain hyper-parameters to higher values greatly faciliates mode swapping,and makes it possible to recover a symetric output. We illustrate our approachwith univariate and bivariate Gaussian mixtures and two real-world datasets.

Suggested Citation

  • Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2010-34
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    References listed on IDEAS

    as
    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    3. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    4. repec:dau:papers:123456789/1906 is not listed on IDEAS
    5. Nicolas Chopin & Tony Lelievre & Gabriel Stoltz, 2010. "Free Energy Methods for Efficient Exploration of Mixture Posterior Densities," Working Papers 2010-33, Center for Research in Economics and Statistics.
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    Cited by:

    1. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Drovandi, Christopher C. & Pettitt, Anthony N. & Henderson, Robert D. & McCombe, Pamela A., 2014. "Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 128-146.

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