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  3. Matematisk datateori

Data-Driven Computational Methods

- Parameter and Operator Estimations
Af: John Harlim Engelsk Hardback

Data-Driven Computational Methods

- Parameter and Operator Estimations
Af: John Harlim Engelsk Hardback
Tjek vores konkurrenters priser
Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.
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Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 168
ISBN-13: 9781108472470
Indbinding: Hardback
Udgave:
ISBN-10: 1108472478
Kategori: Matematisk datateori
Udg. Dato: 12 jul 2018
Længde: 10mm
Bredde: 253mm
Højde: 180mm
Forlag: Cambridge University Press
Oplagsdato: 12 jul 2018
Forfatter(e): John Harlim
Forfatter(e) John Harlim

Kategori Matematisk datateori

ISBN-13 9781108472470

Sprog Engelsk

Indbinding Hardback

Sider 168

Udgave

Længde 10mm

Bredde 253mm

Højde 180mm

Udg. Dato 12 jul 2018

Oplagsdato 12 jul 2018

Forlag Cambridge University Press

Kategori sammenhænge