Computing covariance matrices for constrained nonlinear large scale parameter estimation problems using Krylov subspace methods
In the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design.
Enregistré dans:
| Auteurs principaux: | , |
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| Format: | Chapter/Article |
| Langue: | anglais |
| Publié: |
2012
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| In: |
Constrained Optimization and Optimal Control for Partial Differential Equations
Year: 2011, Pages: 197-212 |
| DOI: | 10.1007/978-3-0348-0133-1_11 |
| Accès en ligne: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-0348-0133-1_11 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-0348-0133-1_11 
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| Notes sur l'auteur: | Ekaterina Kostina, Olga Kostyukova |
| Résumé: | In the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design. |
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| Description: | First online: 28 October 2011 Gesehen am 03.08.2018 |
| Description matérielle: | Online Resource |
| ISBN: | 9783034801331 |
| DOI: | 10.1007/978-3-0348-0133-1_11 |