Abstract
One of the most important properties of a mathematical model is the ability to make predictions: to predict that which has not yet been measured. Such predictions can sometimes be obtained from a simple simulation, but that requires that the parameters in the model are known from before. In biology, the parameters are usually both not known from before and not identifiable, i.e. the parameter values cannot be determined uniquely from available data. In such cases of unidentifiability, the space of acceptable parameters is large, often infinite in certain directions. For such large spaces, sampling-based approaches that try to characterize the entire space have difficulties. Recently, a new type of alternative approaches that circumvent this characterization problem has been proposed: where one only searches those directions in the space of acceptable parameters that are relevant for the uncertainty of a particular prediction. In this review chapter, these recently proposed methods are compared and contrasted, both regarding theoretical properties, and regarding user experience. The focus is on methods from the field of systems biology, but also methods from biostatistics, pharmacodynamics, and biochemometrics are discussed. The hope is that this review will increase the usefulness and understanding of already proposed methods, and thereby help foster a tradition where predictions only are deemed interesting if their uncertainties have been determined.
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Cedersund, G. (2016). Prediction Uncertainty Estimation Despite Unidentifiability: An Overview of Recent Developments. In: Geris, L., Gomez-Cabrero, D. (eds) Uncertainty in Biology. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-21296-8_17
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DOI: https://doi.org/10.1007/978-3-319-21296-8_17
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