Dynamic Data Analysis / Springer Series in Statistics (PDF)
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- Offers an accessible text to those with little or no exposure to differential equations as modeling objects
- Updates and builds on techniques from the popular Functional Data Analysis (Ramsay and Silverman, 2005)
- Opens up new opportunities for dynamical systems and presents additional applications for previously analyzed data
Giles Hooker, PhD, is Associate Professor of Biological Statistics and Computational Biology at Cornell University. In addition to differential equation models, he has published extensively on functional data analysis and uncertainty quantification in machine learning. Much of his methodological work is inspired by collaborations in ecology and citizen science data.
Giles Hooker, PhD, is Associate Professor of Biological Statistics and Computational Biology at Cornell University. In addition to differential equation models, he has published extensively on functional data analysis and uncertainty quantification in machine learning. Much of his methodological work is inspired by collaborations in ecology and citizen science data.
- Autoren: James Ramsay , Giles Hooker
- 2017, 1st ed. 2017, 230 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1493971905
- ISBN-13: 9781493971909
- Erscheinungsdatum: 27.06.2017
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- Größe: 6.23 MB
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