IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v58y2009i5p641-662.html
   My bibliography  Save this article

Realtime sequential inference of static parameters with expensive likelihood calculations

Author

Listed:
  • P. Robins
  • V. E. Rapley
  • N. Green

Abstract

Summary. A methodology is developed for making inference about parameters of a possible covert chemical or biological atmospheric release from sensor readings. The key difficulty in performing this inference is that the results must be obtained in a very short timescale (5 min) to make use of the inference for protection. The methodology that is developed uses some of the components in a sequential Monte Carlo algorithm. However, this inference problem is different from many other sequential Monte Carlo problems, in that there are no state evolution equations, the forward model is highly non‐linear and the likelihoods are non‐Gaussian. The algorithm that is developed can use stored output from complex physics models for more rapid update of the posterior from new data without having to rerun the models. The use of differential evolution Markov chain sampling allows new samples to diverge rapidly from degenerate sample sets. Results for inferences made of atmospheric releases (both real and simulated) of material are presented, demonstrating that the sampling scheme performs adequately despite constraints of a short time span for calculations.

Suggested Citation

  • P. Robins & V. E. Rapley & N. Green, 2009. "Realtime sequential inference of static parameters with expensive likelihood calculations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 641-662, December.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:5:p:641-662
    DOI: 10.1111/j.1467-9876.2009.00670.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2009.00670.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9876.2009.00670.x?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. 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. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    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. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    2. James Martin & Ajay Jasra & Emma McCoy, 2013. "Inference for a class of partially observed point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 413-437, June.
    3. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    4. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2018. "Monte Carlo Confidence Sets for Identified Sets," Econometrica, Econometric Society, vol. 86(6), pages 1965-2018, November.
    5. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Active, or passive? Revisiting the role of fiscal policy in the Great Inflation," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203609, Verein für Socialpolitik / German Economic Association.
    6. Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
    7. Stephanie Ettmeier & Alexander Kriwoluzky, 2020. "Active, or Passive? Revisiting the Role of Fiscal Policy in the Great Inflation," Discussion Papers of DIW Berlin 1872, DIW Berlin, German Institute for Economic Research.
    8. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    9. 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.
    10. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    11. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    12. Lau, F. Din-Houn & Gandy, Axel, 2014. "RMCMC: A system for updating Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 99-110.
    13. 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).
    14. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    15. T. -N. Nguyen & M. -N. Tran & R. Kohn, 2020. "Recurrent Conditional Heteroskedasticity," Papers 2010.13061, arXiv.org, revised Jan 2022.
    16. Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    17. McGrory, C.A. & Pettitt, A.N. & Titterington, D.M. & Alston, C.L. & Kelly, M., 2016. "Transdimensional sequential Monte Carlo using variational Bayes — SMCVB," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 246-254.
    18. Mark Bognanni & Edward P. Herbst, 2014. "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach," Working Papers (Old Series) 1427, Federal Reserve Bank of Cleveland.
    19. C. C. Drovandi & A. N. Pettitt, 2011. "Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation," Biometrics, The International Biometric Society, vol. 67(1), pages 225-233, March.
    20. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.

    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:bla:jorssc:v:58:y:2009:i:5:p:641-662. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.