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Inference for reaction networks using the linear noise approximation

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  • Paul Fearnhead
  • Vasilieos Giagos
  • Chris Sherlock

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  • Paul Fearnhead & Vasilieos Giagos & Chris Sherlock, 2014. "Inference for reaction networks using the linear noise approximation," Biometrics, The International Biometric Society, vol. 70(2), pages 457-466, June.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:2:p:457-466
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    File URL: http://hdl.handle.net/10.1111/biom.12152
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    References listed on IDEAS

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    1. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
    2. Golightly, A. & Wilkinson, D.J., 2008. "Bayesian inference for nonlinear multivariate diffusion models observed with error," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1674-1693, January.
    3. A. Golightly & D. J. Wilkinson, 2005. "Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation," Biometrics, The International Biometric Society, vol. 61(3), pages 781-788, September.
    4. Durham, Garland B & Gallant, A Ronald, 2002. "Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 297-316, July.
    5. Durham, Garland B & Gallant, A Ronald, 2002. "Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 335-338, July.
    6. Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
    7. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
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    Cited by:

    1. Jonathan Fintzi & Jon Wakefield & Vladimir N. Minin, 2022. "A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts," Biometrics, The International Biometric Society, vol. 78(4), pages 1530-1541, December.
    2. Golightly Andrew & Wilkinson Darren J., 2015. "Bayesian inference for Markov jump processes with informative observations," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 169-188, April.
    3. Chris Sherlock, 2021. "Direct statistical inference for finite Markov jump processes via the matrix exponential," Computational Statistics, Springer, vol. 36(4), pages 2863-2887, December.
    4. Wiqvist, Samuel & Golightly, Andrew & McLean, Ashleigh T. & Picchini, Umberto, 2021. "Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    5. Wadkin, Laura E. & Holden, John & Ettelaie, Rammile & Holmes, Melvin J. & Smith, James & Golightly, Andrew & Parker, Nick G. & Baggaley, Andrew W., 2024. "Estimating the reproduction number, R0, from individual-based models of tree disease spread," Ecological Modelling, Elsevier, vol. 489(C).

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