Parameter estimation and uncertainty quantification using information geometry.
epidemic models
inference
likelihood
logistic growth
population models
Journal
Journal of the Royal Society, Interface
ISSN: 1742-5662
Titre abrégé: J R Soc Interface
Pays: England
ID NLM: 101217269
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
entrez:
26
4
2022
pubmed:
27
4
2022
medline:
29
4
2022
Statut:
ppublish
Résumé
In this work, we: (i) review likelihood-based inference for parameter estimation and the construction of confidence regions; and (ii) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification, such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions. All code used in this work to implement the inference and information geometry techniques is available on GitHub.
Identifiants
pubmed: 35472269
doi: 10.1098/rsif.2021.0940
pmc: PMC9042578
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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