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On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability
We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802152/ https://www.ncbi.nlm.nih.gov/pubmed/36714862 http://dx.doi.org/10.1093/pnasnexus/pgac154 |