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Herausgeber: 
  • Karl Sigman
  • Guy Latouche
  • Vaidyanathan Ramaswami
  • Jay Sethuraman
  • Mark S. Squillante
  • David Yao
  • Matrix-Analytic Methods in Stochastic Models 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2012  
    Genre:  Schulbücher 
     
    C / Management & management techniques / Management science / Mathematics and Statistics / Numerical analysis / Operational research / Operations Research / Operations Research, Management Science
    ISBN:  9781461449089 
    EAN-Code: 
    9781461449089 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #27 - Springer Proceedings in Mathematics & Statistics  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  5325 gr 
    Seiten:  258 
    Illustration:  XIV, 258 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Matrix-analytic and related methods have become recognized as an important and fundamental approach for the mathematical analysis of general classes of complex stochastic models.  Research in the area of matrix-analytic and related methods seeks to discover underlying probabilistic structures intrinsic in such stochastic models, develop numerical algorithms for computing functionals (e.g., performance measures) of the underlying stochastic processes, and apply these probabilistic structures and/or computational algorithms within a wide variety of fields.  This volume presents recent research results on: the theory, algorithms and methodologies concerning matrix-analytic and related methods in stochastic models; and the application of matrix-analytic and related methods in various fields, which includes but is not limited to computer science and engineering, communication networks and telephony, electrical and industrial engineering, operations research, management science, financial and risk analysis, and bio-statistics.  These research studies provide deep insights and understanding of the stochastic models of interest from a mathematics and applications perspective, as well as identify directions for future research.

      



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