
Markov Chains and Stochastic Stability
Springer (Publisher)
Published on 12. December 2012
Book
Paperback/Softback
XVI, 550 pages
978-1-4471-3269-1 (ISBN)
Description
Markov Chains and Stochastic Stability
is part of the
Communications and Control Engineering Series
(
CCES
) edited by Professors B.W. Dickinson, E.D. Sontag, M. Thoma, A. Fettweis, J.L. Massey and J.W. Modestino. The area of Markov chain theory and application has matured over the past 20 years into something more accessible and complete. It is of increasing interest and importance. This publication deals with the action of Markov chains on general state spaces. It discusses the theories and the use to be gained, concentrating on the areas of engineering, operations research and control theory. Throughout, the theme of stochastic stability and the search for practical methods of verifying such stability, provide a new and powerful technique. This does not only affect applications but also the development of the theory itself. The impact of the theory on specific models is discussed in detail, in order to provide examples as well as to demonstrate the importance of these models.
Markov Chains and Stochastic Stability
can be used as a textbook on applied Markov chain theory, provided that one concentrates on the main aspects only. It is also of benefit to graduate students with a standard background in countable space stochastic models. Finally, the book can serve as a research resource and active tool for practitioners.
More details
Series
Edition
1993 ed.
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
10 s/w Abbildungen
XVI, 550 p. 10 illus.
Dimensions
Height: 216 mm
Width: 140 mm
Thickness: 31 mm
Weight
710 gr
ISBN-13
978-1-4471-3269-1 (9781447132691)
DOI
10.1007/978-1-4471-3267-7
Schweitzer Classification
Other editions
Additional editions

Sean P. Meyn | Richard L. Tweedie
Markov Chains and Stochastic Stability
Book
03/1996
Springer
€85.55
Article exhausted; check different version
Content
I Communication and Regeneration.- 1 Heuristics.- 2 Markov Models.- 3 Transition Probabilities.- 4 Irreducibility.- 5 Pseudo-atoms.- 6 Topology and Continuity.- 7 The Nonlinear State Space Model.- II Stability Structures.- 8 Transience and Recurrence.- 9 Harris and Topological Recurrence.- 10 The Existence of ?.- 11 Drift and Regularity.- 12 Invariance and Tightness.- III Convergence.- 13 Ergodicity.- 14 ƒ-Ergodicity and ƒ-Regularity.- 15 Geometric Ergodicity.- 16 V-Uniform Ergodicity.- 17 Sample Paths and Limit Theorems.- 18 Positivity.- 19 Generalized Classification Criteria.- IV Appendices.- A Mud Maps.- A.l Recurrence versus transience.- A.2 Positivity versus nullity.- A.3 Convergence Properties.- B Testing for Stability.- B.l A Glossary of Drift Conditions.- B.2 The scalar SETAR Model: a complete classification.- C A Glossary of Model Assumptions..- C.l Regenerative Models.- C.2 State Space Models.- D Some Mathematical Background.- D.l Some Measure Theory.- D.2 Some Probability Theory.- D.3 Some Topology.- D.4 Some Real Analysis.- D.5 Some Convergence Concepts for Measures.- D.6 Some Martingale Theory.- D.7 Some Results on Sequences and Numbers.- References.- Symbols Index.