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Comparative Statistical Analysis of Qualitative Parametrization Sets

Part of the Lecture Notes in Computer Science book series (LNBI,volume 9271)


The problem of model parametrization is a core issue for all varieties of mathematical modelling in biology. This problem becomes more tractable when qualitative modelling is used, since the range of parameter values is finite and consequently it is possible to enumerate and evaluate all possible parametrizations of a model. If such an approach is undertaken, one usually obtains a vast set of parametrizations that are scored for various properties, e.g. fitness. The usual next step is to take the best scoring parametrization. However, as noted in recent works [1, 4], there is knowledge to be gained from examining sets of parametrizations based on their scoring. In this article we extend this line of thought and introduce a comprehensive workflow for comparing such sets and obtaining knowledge from the comparison.


  • Qualitative modelling
  • Statistical inference
  • Big data
  • Parameter identification
  • Data mining

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Streck, A., Thobe, K., Siebert, H. (2015). Comparative Statistical Analysis of Qualitative Parametrization Sets. In: Abate, A., Šafránek, D. (eds) Hybrid Systems Biology. HSB 2015. Lecture Notes in Computer Science(), vol 9271. Springer, Cham.

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  • Print ISBN: 978-3-319-26915-3

  • Online ISBN: 978-3-319-26916-0

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