Predicting the Behavior of Sparsely-Sampled Systems Across Neurobiology and Epidemiology.
Inference
Neurobiology
Journal
Bulletin of mathematical biology
ISSN: 1522-9602
Titre abrégé: Bull Math Biol
Pays: United States
ID NLM: 0401404
Informations de publication
Date de publication:
31 08 2023
31 08 2023
Historique:
received:
16
12
2022
accepted:
30
05
2023
medline:
4
9
2023
pubmed:
1
9
2023
entrez:
31
8
2023
Statut:
epublish
Résumé
Inference is a term that encompasses many techniques including statistical data assimilation (SDA). Unlike machine learning, which is designed to harness predictive power from extremely large data sets, SDA is designed for sparsely-sampled systems. This is the realm of study of nonlinear dynamical systems in nature. Formulated as an optimization procedure, SDA can be considered a path-integral approach to state and parameter estimation. Within this formulation, we can use the physical principle of least action to identify optimal solutions: solutions that are consistent with both measurements and a dynamical model assumed to give rise to those measurements. I review examples from neurobiology and an epidemiological model tailored to the coronavirus SARS-CoV-2, to demonstrate the versatility of SDA across the sciences, and how these distinct applications possess commonalities that can inform one another.
Identifiants
pubmed: 37653124
doi: 10.1007/s11538-023-01176-x
pii: 10.1007/s11538-023-01176-x
doi:
Types de publication
Review
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
91Informations de copyright
© 2023. The Author(s), under exclusive licence to Society for Mathematical Biology.
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