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Autor(en): 
  • James Ramsay
  • Giles Hooker
  • Dynamic Data Analysis: Modeling Data with Differential Equations 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Juli 2018  
    Genre:  Schulbücher 
     
    Angewandte Mathematik / Applications of Mathematics / Applied mathematics / B / Big Data / Big Data/Analytics / Business mathematics & systems / Data Analysis and Big Data
    ISBN:  9781493984121 
    EAN-Code: 
    9781493984121 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Springer Series in Statistics  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  385 gr 
    Seiten:  230 
    Illustration:  XVII, 230 p. 84 illus., 50 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.

      



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