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A Bayesian approach to estimate parameters of ordinary differential equation

Author

Listed:
  • Hanwen Huang

    (University of Georgia)

  • Andreas Handel

    (University of Georgia)

  • Xiao Song

    (University of Georgia)

Abstract

We develop a Bayesian approach to estimate the parameters of ordinary differential equations (ODE) from the observed noisy data. Our method does not need to solve ODE directly. We replace the ODE constraint with a probability expression and combine it with the nonparametric data fitting procedure into a joint likelihood framework. One advantage of the proposed method is that for some ODE systems, one can obtain closed form conditional posterior distributions for all variables which substantially reduce the computational cost and facilitate the convergence process. An efficient Riemann manifold based hybrid Monte Carlo scheme is implemented to generate samples for variables whose conditional posterior distribution cannot be written in terms of closed form. Our approach can be applied to situations where the state variables are only partially observed. The usefulness of the proposed method is demonstrated through applications to both simulated and real data.

Suggested Citation

  • Hanwen Huang & Andreas Handel & Xiao Song, 2020. "A Bayesian approach to estimate parameters of ordinary differential equation," Computational Statistics, Springer, vol. 35(3), pages 1481-1499, September.
  • Handle: RePEc:spr:compst:v:35:y:2020:i:3:d:10.1007_s00180-020-00962-8
    DOI: 10.1007/s00180-020-00962-8
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    References listed on IDEAS

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    1. Xiaolei Xun & Jiguo Cao & Bani Mallick & Arnab Maity & Raymond J. Carroll, 2013. "Parameter Estimation of Partial Differential Equation Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1009-1020, September.
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    6. Yangxin Huang & Hulin Wu, 2006. "A Bayesian approach for estimating antiviral efficacy in HIV dynamic models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(2), pages 155-174.
    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. Hanwen Huang, 2022. "Bayesian multi‐level mixed‐effects model for influenza dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1978-1995, November.

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