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Journal ArticleDOI

Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes

Sean P. Meyn, +1 more
- 01 Sep 1993 - 
- Vol. 25, Iss: 3, pp 518-548
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TLDR
In this paper, the authors developed criteria for continuous-parameter Markovian processes on general state spaces, based on Foster-Lyapunov inequalities for the extended generator, and applied the criteria to several specific processes, including linear stochastic systems under nonlinear feedback, work-modulated queues, general release storage processes and risk processes.
Abstract
In Part I we developed stability concepts for discrete chains, together with Foster–Lyapunov criteria for them to hold. Part II was devoted to developing related stability concepts for continuous-time processes. In this paper we develop criteria for these forms of stability for continuous-parameter Markovian processes on general state spaces, based on Foster-Lyapunov inequalities for the extended generator. Such test function criteria are found for non-explosivity, non-evanescence, Harris recurrence, and positive Harris recurrence. These results are proved by systematic application of Dynkin's formula. We also strengthen known ergodic theorems, and especially exponential ergodic results, for continuous-time processes. In particular we are able to show that the test function approach provides a criterion for f-norm convergence, and bounding constants for such convergence in the exponential ergodic case. We apply the criteria to several specific processes, including linear stochastic systems under non-linear feedback, work-modulated queues, general release storage processes and risk processes.

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Citations
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Markov Chains and Stochastic Stability

TL;DR: This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background.

Sous la direction de

TL;DR: Mostafa Adimy as mentioned in this paper Directeur de Recherches à l’INRIA Dir. de thèse Ionel S. CIUPERCA Mâıtre de Conférence à l'Université Lyon 1 Examinateur Michael C. MACKEY Directeur of Recherche et al.
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Control Techniques for Complex Networks

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Journal ArticleDOI

Stability of Markovian processes II: continuous-time processes and sampled chains

TL;DR: In this paper, the authors extend the results of Meyn and Tweedie (1992b) from discrete-time parameter to continuous-parameter Markovian processes evolving on a topological space, and prove connections between these and standard probabilistic recurrence concepts.
Journal ArticleDOI

Exponential and Uniform Ergodicity of Markov Processes

TL;DR: In this article, the authors developed a similar theory for continuous time processes and considered the following types of criteria for geometric convergence: 1. The existence of exponentially bounded hitting times on one and then all suitably "small" sets; 2. The presence of "Foster-Lyapunov" or "drift" conditions on the extended generator of the process.
References
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Book

Stochastic processes

J. L. Doob, +1 more
Book

Markov Chains and Stochastic Stability

TL;DR: This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background.
Book

Stochastic Stability of Differential Equations

TL;DR: In this article, the authors define the boundedness in probability and stability of Stochastic Processes Defined by Differential Equations (SDEs) defined by Markov Processes.
Book

Markov Models & Optimization

TL;DR: In this article, the authors present a new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems, based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stocha- system models.
Journal ArticleDOI

Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models

TL;DR: Stochastic calculus for these stochastic processes is developed and a complete characterization of the extended generator is given; this is the main technical result of the paper.