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vSMC: Parallel Sequential Monte Carlo in C++

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  • Zhou, Yan

Abstract

Sequential Monte Carlo is a family of algorithms for sampling from a sequence of distributions. Some of these algorithms, such as particle filters, are widely used in physics and signal processing research. More recent developments have established their application in more general inference problems such as Bayesian modeling. These algorithms have attracted considerable attention in recent years not only be- cause that they have desired statistical properties, but also because they admit natural and scalable parallelization. However, they are perceived to be difficult to implement. In addition, parallel programming is often unfamiliar to many researchers though conceptually appealing. A C++ template library is presented for the purpose of implementing generic sequential Monte Carlo algorithms on parallel hardware. Two examples are presented: a simple particle filter and a classic Bayesian modeling problem.

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  • Zhou, Yan, 2015. "vSMC: Parallel Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i09).
    • Handle: RePEc:jss:jstsof:v:062:i09
    DOI: http://hdl.handle.net/10.18637/jss.v062.i09
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    References listed on IDEAS

    as
    1. Johansen, Adam M., 2009. "SMCTC: Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i06).
    2. repec:dau:papers:123456789/1908 is not listed on IDEAS
    3. Ajay Jasra & David A. Stephens & Arnaud Doucet & Theodoros Tsagaris, 2011. "Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 1-22, March.
    4. Calderhead, Ben & Girolami, Mark, 2009. "Estimating Bayes factors via thermodynamic integration and population MCMC," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4028-4045, October.
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