Parameter identifiability, parameter estimation and model prediction for differential equation models
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913718978215936 |
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| author | Simpson, Matthew J Baker, Ruth E |
| author_facet | Simpson, Matthew J Baker, Ruth E |
| contents | 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 likelihood-based 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08177 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter identifiability, parameter estimation and model prediction for differential equation models Simpson, Matthew J Baker, Ruth E Methodology 97M10, 97M50, 00A71 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 likelihood-based 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. |
| title | Parameter identifiability, parameter estimation and model prediction for differential equation models |
| topic | Methodology 97M10, 97M50, 00A71 |
| url | https://arxiv.org/abs/2405.08177 |