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Results for Linear noise approximation

Publications & Outputs

  1. Accelerating inference for stochastic kinetic models

    Lowe, T. E., Golightly, A. & Sherlock, C., 30/09/2023, In: Computational Statistics and Data Analysis. 185, 21 p., 107760.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  2. Efficient sampling of conditioned Markov jump processes

    Golightly, A. & Sherlock, C. G., 13/02/2019, (E-pub ahead of print) In: Statistics and Computing. 15 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  3. Improved bridge constructs for stochastic differential equations

    Whitaker, G. A., Golightly, A., Boys, R. J. & Sherlock, C. G., 07/2017, In: Statistics and Computing. 27, 4, p. 885-900 16 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  4. Efficient particle MCMC for exact inference in stochastic biochemical network models through approximation of expensive likelihoods

    Golightly, A., Henderson, D. & Sherlock, C., 09/2015, In: Statistics and Computing. 25, 5, p. 1039-1055 17 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  5. Inference for reaction networks using the linear noise approximation

    Fearnhead, P., Giagos, V. & Sherlock, C., 2014, In: Biometrics. 70, p. 457-466 10 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review