Controversy in mechanistic modelling with Gaussian processes

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Macdonald, Benn; Higham, Catherine; Husmeier, Dirk;
  • Publisher: PMLR

Parameter inference in mechanistic models based on non-affine differential equations is computationally onerous, and various faster alternatives based on gradient matching have been proposed. A particularly promising approach is based on nonparametric Bayesian modelling... View more
  • References (19)
    19 references, page 1 of 2

    Atkins, P. W. Physical Chemistry. Oxford University Press, Oxford, 3rd edition, 1986.

    Babtie, A.C, Kirk, P., and Stumpf, M.P.H. Topological sensitivity analysis for systems biology. PNAS, 111(51): 18507-18512, December 2014.

    Bishop, C.M. Pattern Recognition and Machine Learning. Springer, Singapore, 2006. ISBN 978-0387-31073-2.

    Calderhead, B., Girolami, M., and Lawrence, N.D. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes. Neural Information Processing Systems (NIPS), 22, 2008.

    Campbell, D. and Steele, R.J. Smooth functional tempering for nonlinear differential equation models. Stat Comput, 22:429-443, 2012.

    Dondelinger, F., Filippone, M., Rogers, S, and Husmeier, D. ODE parameter inference using adaptive gradient matching with Gaussian processes. Journal of Machine Learning Research - Workshop and Conference Proceedings: The 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 31:216-228, 2013.

    FitzHugh, R. Impulses and physiological states in models of nerve membrane. Biophys. J., 1:445-466, 1961.

    Gelman, A. and Rubin, D.B. Inference from iterative simulation using multiple sequences. Statistical Science, 7: 457-472, 1992.

    Gonza´lez, J., Vujacˇic´, I., and Wit, E. Inferring latent gene regulatory network kinetics. Statistical Applications in Genetics and Molecular Biology, 12(1):109-127, 2013.

    Graepel, T. Solving noisy linear operator equations by Gaussian processes: Application to ordinary and partial differential equations. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pp. 234-241, 2003.

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