Markov Chains: With Stationary Transition Probabilities (Grundlehren der mathematischen Wissenschaften)

Chung, Kai Lai

ISBN 10: 3642620175 / ISBN 13: 9783642620171

Editore: Springer, 2012

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Dati bibliografici

• Titolo: Markov Chains: With Stationary Transition Probabilities (Grundlehren der mathematischen Wissenschaften)
• Editore: Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 3642620175
• ISBN 13: 9783642620171
• Rilegatura: Brossura
• Edizione: seconda edizione
• Condizione: New

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Riassunto

From the reviews:

J. Neveu, 1962 in Zentralblatt für Mathematik, 92.Band Heft 2, p. 343: "Ce livre écrit par l'un des plus éminents spécialistes en la matière, est un exposé très détaillé de la théorie des processus de Markov définis sur un espace dénombrable d'états et homogènes dans le temps (chaines stationnaires de Markov)."

N.Jain, 2008 in Selected Works of Kai Lai Chung, edited by Farid AitSahlia (University of Florida, USA), Elton Hsu (Northwestern University, USA), & Ruth Williams (University of California-San Diego, USA), Chapter 1, p. 15: "This monograph deals with countable state Markov chains in both discrete time (Part I) and continuous time (Part II). [...] Much of Kai Lai's fundamental work in the field is included in this monograph. Here, for the first time, Kai Lai gave a systematic exposition of the subject which includes classification of states, ratio ergodic theorems, and limit theorems for functionals of the chain."

Contenuti

I. Discrete Parameter.- § 1. Fundamental definitions.- § 2. Transition probabilities.- § 3. Classification of states.- § 4. Recurrence.- § 5. Criteria and examples.- § 6. The main limit theorem.- § 7. Various complements.- § 8. Repetitive pattern and renewal process.- § 9. Taboo probabilities.- § 10. The generating function.- § 11. The moments of first entrance time distributions.- § 12. A random walk example.- § 13. System theorems.- § 14. Functionals and associated random variables.- § 15. Ergodic theorems.- § 16. Further limit theorems.- § 17. Almost closed and sojourn sets.- II. Continuous Parameter.- § 1. Transition matrix: basic properties.- § 2. Standard transition matrix.- § 3. Differentiability.- § 4. Definitions and measure-theoretic foundations.- § 5. The sets of constancy.- § 6. Continuity properties of sample functions.- § 7. Further specifications of the process.- § 8. Optional random variable.- § 9. Strong Markov property.- § 10. Classification of states.- § 11. Taboo probability functions.- § 12. Last exit time.- § 13. Ratio limit theorems; discrete approximations.- § 14. Functionals.- § 15. Post-exit process.- § 16. Imbedded renewal process.- § 17. The two systems of differential equations.- § 18. The minimal solution.- § 19. The first infinity.- § 20. Examples.