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SMCTC: Sequential Monte Carlo in C++

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  • Johansen, Adam M.

Abstract

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation.

Suggested Citation

  • Johansen, Adam M., 2009. "SMCTC: Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i06).
    • Handle: RePEc:jss:jstsof:v:030:i06
    DOI: http://hdl.handle.net/10.18637/jss.v030.i06
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    1. repec:dau:papers:123456789/6072 is not listed on IDEAS
    2. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
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    Cited by:

    1. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    2. Murray, Lawrence M., 2015. "Bayesian State-Space Modelling on High-Performance Hardware Using LibBi," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i10).
    3. Zhou, Yan, 2015. "vSMC: Parallel Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i09).

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