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Springer Dynamic Data Analysis - Modeling Differential Equations

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Description

Explore the intersection of functional data analysis and dynamical systems with this Springer publication. This text focuses on using smoothing methods to develop and estimate differential equations, building upon the techniques established in Ramsay and Silverman (2005) Functional Data Analysis. By treating a dynamical system as a buffer that translates sudden input changes into smooth, controlled output responses, this book opens new opportunities for analyzing previously studied data. Designed for accessibility, the technical level is kept low to accommodate readers with little or no prior exposure to differential equations, making it a valuable resource for those entering the field of data modeling.

Key Features

This Springer Series in Statistics text focuses on smoothing methods for developing and estimating differential equations.

The content builds on established techniques from Ramsay and Silverman (2005) Functional Data Analysis.

Learn how dynamical systems act as buffers to translate sudden input changes into smooth controlled output responses.

The book provides new opportunities for applying previously analyzed data to dynamical systems.

The technical level is kept low to support readers with little or no exposure to differential equations.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
28 June 2017
Listed Since
18 April 2017

Barcode

No barcode data available

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