# Reversibility and stochastic networks / Frank P. Kelly

Type de document : MonographieCollection : Wiley series in probability and mathematical statisticsLangue : anglais.Pays : Grande Bretagne.Éditeur : Chichester : John Wiley & Sons, 1997Description : 1 vol. (VIII-230 p.) ; 24 cmISBN : 0471276014.ISSN : 0277-2728.Bibliographie : Bibliogr. p. 217-222. Index.Sujet MSC : 60K20, Probability theory and stochastic processes -- Special processes, Applications of Markov renewal processes (reliability, queueing networks, etc.)60J27, Probability theory and stochastic processes -- Markov processes, Continuous-time Markov processes on discrete state spaces

92D10, Biology and other natural sciences -- Genetics and population dynamics, Genetics

92D25, Biology and other natural sciences -- Genetics and population dynamics, Population dynamics (general)

92Exx, Biology and other natural sciences, ChemistryEn-ligne : site de l'auteur | Zentralblatt | MathSciNet

Current location | Call number | Status | Date due | Barcode |
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CMI Salle R | 60 KEL (Browse shelf) | Available | 11678-01 |

Bibliogr. p. 217-222. Index

The stationary distribution of an ergodic reversible Markov chain can usually be obtained quite easily from the determining system of linear equations, the so-called global balance equations. This is due to the fact that, in the presence of reversibility, these equations break up into further, so-called local or partial balance equations which are also satisfied by the stationary law. This book is devoted to the study of mostly-continuous-time-Markov chains being reversible or satisfying some type of partial balance. Essentially, the book constitutes an account – the first of its kind – of the many uses of reversibility and partial balance. Already in Chapter 1, which presents the basic facts about reversible Markov chains, many illustrative examples are given. The remaining chapters, 2–9, are devoted to the presentation and investigation of certain classes of examples from numerous fields or application such as population dynamics and genetics (Chapters 2, 5–9), chemistry (Chapter 8), queuing theory (Chapters 2–4), and others. A common aspect of these examples is the fact that they can all be viewed as some kind of stochastic network, a point of view which allows a fairly unified statement of the subject matter. The book is recommended to anyone interested in Markov chain models. It is a rich source of examples for students and teachers, of ideas for research workers. The prerequisite is an understanding of Markov processes at about the level of Feller’s Introduction to Probability Theory and its Applications, Vol. I. (Zentralblatt)

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