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Non-homogeneous random walks : Lyapunov function methods for near-critical stochastic systems /

Stochastic systems provide powerful abstract models for a variety of important real-life applications: for example, power supply, traffic flow, data transmission. They (and the real systems they model) are often subject to phase transitions, behaving in one way when a parameter is below a certain cr...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Menʹshikov, M. V. (Mikhail Vasilʹevich) (Autor), Popov, Serguei, 1972- (Autor), Wade, Andrew (Andrew R.), 1981- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge : Cambridge University Press, 2017.
Colección:Cambridge tracts in mathematics ; 209.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Menʹshikov, M. V.  |q (Mikhail Vasilʹevich),  |e author. 
245 1 0 |a Non-homogeneous random walks :  |b Lyapunov function methods for near-critical stochastic systems /  |c Mikhail Menshikov, University of Durham ; Serguei Popov, Universidade Estadual de Campinas, Brazil ; Andrew Wade, University of Durham. 
264 1 |a Cambridge :  |b Cambridge University Press,  |c 2017. 
264 4 |c ©2017 
300 |a 1 online resource (xviii, 363 pages) :  |b illustrations 
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490 1 |a Cambridge tracts in mathematics ;  |v 209 
520 |a Stochastic systems provide powerful abstract models for a variety of important real-life applications: for example, power supply, traffic flow, data transmission. They (and the real systems they model) are often subject to phase transitions, behaving in one way when a parameter is below a certain critical value, then switching behaviour as soon as that critical value is reached. In a real system, we do not necessarily have control over all the parameter values, so it is important to know how to find critical points and to understand system behaviour near these points. This book is a modern presentation of the 'semimartingale' or 'Lyapunov function' method applied to near-critical stochastic systems, exemplified by non-homogeneous random walks. Applications treat near-critical stochastic systems and range across modern probability theory from stochastic billiards models to interacting particle systems. Spatially non-homogeneous random walks are explored in depth, as they provide prototypical near-critical systems. 
504 |a Includes bibliographical references and index. 
505 0 |a Introduction -- Semimartingale approach and Markov chains -- Lamperti's problem -- Many-dimensional random walks -- Heavy tails -- Further applications -- Markov chains in continuous time. 
588 0 |a Print version record. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Random walks (Mathematics) 
650 0 |a Stochastic processes. 
650 6 |a Marches aléatoires (Mathématiques) 
650 6 |a Processus stochastiques. 
650 7 |a MATHEMATICS  |x Applied.  |2 bisacsh 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Random walks (Mathematics)  |2 fast 
650 7 |a Stochastic processes  |2 fast 
700 1 |a Popov, Serguei,  |d 1972-  |e author. 
700 1 |a Wade, Andrew  |q (Andrew R.),  |d 1981-  |e author. 
776 0 8 |i Print version:  |a Menʹshikov, M.V. (Mikhail Vasilʹevich).  |t Non-homogeneous random walks.  |d Cambridge, United Kingdom : Cambridge University Press, 2017  |z 9781107026698  |w (DLC) 2016036262  |w (OCoLC)956946977 
830 0 |a Cambridge tracts in mathematics ;  |v 209. 
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