Considering discrepancy when calibrating a mechanistic electrophysiology model.
Bayesian inference
cardiac model
model discrepancy
uncertainty quantification
Journal
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
ISSN: 1471-2962
Titre abrégé: Philos Trans A Math Phys Eng Sci
Pays: England
ID NLM: 101133385
Informations de publication
Date de publication:
12 Jun 2020
12 Jun 2020
Historique:
entrez:
26
5
2020
pubmed:
26
5
2020
medline:
2
3
2021
Statut:
ppublish
Résumé
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed
Identifiants
pubmed: 32448065
doi: 10.1098/rsta.2019.0349
pmc: PMC7287333
doi:
Substances chimiques
Ion Channels
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20190349Subventions
Organisme : Wellcome Trust
ID : 212203/Z/18/Z
Pays : United Kingdom
Organisme : British Heart Foundation
ID : PG/15/59/31621
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/13/4/30184
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/18/6/33805
Pays : United Kingdom
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