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Parameter Identifiability, Parameter Estimation, and Model Prediction for Differential Equation Models
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DOI：10.1137/24M1667968.png)
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Abstract
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Interpreting data with mathematical models is an important aspect of real-world industrial and applied mathematical modeling. Often we are interested to understand the extent to which a particular set of data informs and constrains model parameters. This question is closely related to the concept of parameter identifiability, and in this article we present a series of computational exercises to introduce tools that can be used to assess parameter identifiability, estimate parameters, and generate model predictions. Taking a likelihoodbased approach, we show that very similar ideas and algorithms can be used to deal with a range of different mathematical modeling frameworks. The exercises and results presented in this article are supported by a suite of open access codes that can be accessed on GitHub.
Keywords:
mathematical modeling
parameter inference
identifiability analysis
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