IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v062i09.html
   My bibliography  Save this article

vSMC: Parallel Sequential Monte Carlo in C++

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v062i09/v62i09.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v062i09/vSMC.zip
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v062.i09?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
    2. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    3. Will Penny & Biswa Sengupta, 2016. "Annealed Importance Sampling for Neural Mass Models," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-25, March.
    4. Yan-Feng Wu & Xiangyu Yang & Jian-Qiang Hu, 2024. "Method of Moments Estimation for Affine Stochastic Volatility Models," Papers 2408.09185, arXiv.org.
    5. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    6. Marko Mlikota & Frank Schorfheide, 2022. "Sequential Monte Carlo With Model Tempering," Papers 2202.07070, arXiv.org.
    7. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    8. Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021. "Online estimation of DSGE models," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 33-58.
    9. Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
    10. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    11. 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.
    12. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    13. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    14. Speich, Matthias & Dormann, Carsten F. & Hartig, Florian, 2021. "Sequential Monte-Carlo algorithms for Bayesian model calibration – A review and method comparison✰," Ecological Modelling, Elsevier, vol. 455(C).
    15. Marco Grzegorczyk & Andrej Aderhold & Dirk Husmeier, 2017. "Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration," Computational Statistics, Springer, vol. 32(2), pages 717-761, June.
    16. Sophie Donnet & Stéphane Robin, 2021. "Accelerating Bayesian estimation for network Poisson models using frequentist variational estimates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 858-885, August.
    17. Filippone, Maurizio & Sanguinetti, Guido, 2011. "Approximate inference of the bandwidth in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3104-3122, December.
    18. Duffield, Samuel & Singh, Sumeetpal S., 2022. "Ensemble Kalman inversion for general likelihoods," Statistics & Probability Letters, Elsevier, vol. 187(C).
    19. Liu Baisen & Wang Liangliang & Cao Jiguo, 2018. "Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 117-127, June.
    20. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:062:i09. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.