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Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells

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  • Matthew Plumlee
  • V. Roshan Joseph
  • Hui Yang

Abstract

Computational modeling is a popular tool to understand a diverse set of complex systems. The output from a computational model depends on a set of parameters that are unknown to the designer, but a modeler can estimate them by collecting physical data. In the described study of the ion channels of ventricular myocytes, the parameter of interest is a function as opposed to a scalar or a set of scalars. This article develops a new modeling strategy to nonparametrically study the functional parameter using Bayesian inference with Gaussian process prior distributions. A new sampling scheme is devised to address this unique problem.

Suggested Citation

  • Matthew Plumlee & V. Roshan Joseph & Hui Yang, 2016. "Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 500-509, April.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:514:p:500-509
    DOI: 10.1080/01621459.2015.1119695
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    References listed on IDEAS

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    1. Pablo Achard & Erik De Schutter, 2006. "Complex Parameter Landscape for a Complex Neuron Model," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
    2. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    3. 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.
    4. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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    Cited by:

    1. Matthew Plumlee, 2017. "Bayesian Calibration of Inexact Computer Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1274-1285, July.

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