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Continuous-Time Markov Chains

  • Richard SerfozoEmail author
Chapter
  • 2.8k Downloads
Part of the Probability and Its Applications book series (PIA)

Abstract

A continuous-time Markov chain (CTMC) is a discrete-time Markov chain with the modification that, instead of spending one time unit in a state, it remains in a state for an exponentially distributed time whose rate depends on the state. The methodology of CTMCs is based on properties of renewal and Poisson processes as well as discrete-time chains. CTMCs are natural candidates for modeling systems in real time such as production and inventory systems, computer and telecommunications networks, and miscellaneous input-output systems. Many continuous-time processes have discretetime analogues; for instance, birth-death and Brownian motion processes are continuous-time analogues of discrete-time random walks. One’s choice of a continuous- or discrete-time model for a system typically depends on how realistic it is, its ease in addressing the issues at hand, or in computing quantities of interest.

Keywords

Markov Chain Stationary Distribution Invariant Measure Transition Rate Poisson Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Georgia Institute of TechnologySchool of Industrial & Systems EngineeringAtlantaUSA

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