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Dynamical systems are frequently used to model biological systems. When these models are fit to data, it is necessary to ascertain the uncertainty in the model fit. Here, we present

Dynamical systems are frequently used to model biological systems. When these models are fit to data, it is necessary to ascertain the uncertainty in the model fit. Here, we present prediction deviation, a metric of uncertainty that determines the extent to which observed data have constrained the model's predictions. This is accomplished by solving an optimization problem that searches for a pair of models that each provides a good fit for the observed data, yet has maximally different predictions.

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Date Created
  • 2016-06-14
Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1063/1.4953795
    • Identifier Type
      International standard serial number
      Identifier Value
      0950-0618
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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Letham, B., Letham, P. A., Rudin, C., & Browne, E. P. (2016). Prediction uncertainty and optimal experimental design for learning dynamical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(6), 063110. doi:10.1063/1.4953795

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