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Autor(en): 
  • G. George Yin
  • Qing Zhang
  • Discrete-Time Markov Chains: Two-Time-Scale Methods and Applications 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2010  
    Genre:  Schulbücher 
     
    Applications of Mathematics / Applied mathematics / Automatic control engineering / C / Control engineering / Control, Robotics, Automation / Control, Robotics, Mechatronics / Decision Making
    ISBN:  9781441919557 
    EAN-Code: 
    9781441919557 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #55 - Stochastic Modelling and Applied Probability  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  563 gr 
    Seiten:  347 
    Illustration:  XX, 347 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Focusing on discrete-time-scale Markov chains, the contents of this book are an outgrowth of some of the authors' recent research.  The motivation stems from existing and emerging applications in optimization and control of complex hybrid Markovian systems in manufacturing, wireless communication, and financial engineering.  Much effort in this book is devoted to designing system models arising from these applications, analyzing them via analytic and probabilistic techniques, and developing feasible computational algorithms so as to reduce the inherent complexity.

    This book presents results including asymptotic expansions of probability vectors, structural properties of occupation measures, exponential bounds, aggregation and decomposition and associated limit processes, and interface of discrete-time and continuous-time systems.  One of the salient features is that it contains a diverse range of applications on filtering, estimation, control, optimization, and Markov decision processes, and financial engineering.  This book will be an important reference for researchers in the areas of applied probability, control theory, operations research, as well as for practitioners who use optimization techniques.  Part of the book can also be used in a graduate course of applied probability, stochastic processes, and applications.

      



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