Band 37
Continuous-Time Markov Chains and Applications A Two-Time-Scale Approach
Aus der Reihe
Stochastic Modelling and Applied Probability
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- Hardcover
- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
128,39 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
14.11.2012
Verlag
Springer New YorkSeitenzahl
430 (Printausgabe)
Dateigröße
4052 KB
Auflage
2nd ed. 2013
Sprache
Englisch
EAN
9781461443469
This book gives a systematic treatment of singularly perturbed systems that naturally arise in control and optimization, queueing networks, manufacturing systems, and financial engineering. It presents results on asymptotic expansions of solutions of Komogorov forward and backward equations, properties of functional occupation measures, exponential upper bounds, and functional limit results for Markov chains with weak and strong interactions. To bridge the gap between theory and applications, a large portion of the book is devoted to applications in controlled dynamic systems, production planning, and numerical methods for controlled Markovian systems with large-scale and complex structures in the real-world problems. This second edition has been updated throughout and includes two new chapters on asymptotic expansions of solutions for backward equations and hybrid LQG problems. The chapters on analytic and probabilistic properties of two-time-scale Markov chains have been almost completely rewritten and the notation has been streamlined and simplified.
This book is written for applied mathematicians, engineers, operations researchers, and applied scientists. Selected material from the book can also be used for a one semester advanced graduate-level course in applied probability and stochastic processes.
This book is written for applied mathematicians, engineers, operations researchers, and applied scientists. Selected material from the book can also be used for a one semester advanced graduate-level course in applied probability and stochastic processes.
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