Markov Chains

  • Christos G. Cassandras
  • Stéphane Lafortune
Part of the The Kluwer International Series on Discrete Event Dynamic Systems book series (DEDS, volume 11)

Abstract

In the previous chapter, we presented the Generalized Semi-Markov Process (GSMP) framework as a means of modeling stochastic DES. By allowing event clocks to tick at varying speeds, we also provided an extension to the basic GSMP. In addition, we introduced the Poisson process as a basic building block for a class of stochastic DES which possess the Markov (memoryless) property. Thus, we obtained the class of stochastic processes known as Markov chains, which we will study in some detail in this chapter. It should be pointed out that the analysis of Markov chains provides a rich framework for studying many DES of practical interest, ranging from gambling and the stock market to the design of “high-tech” computer systems and communication networks.

Keywords

Markov Chain Markov Chain Model Transition Probability Matrix State Transition Diagram Recurrent State 
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 Science+Business Media New York 1999

Authors and Affiliations

  • Christos G. Cassandras
    • 1
  • Stéphane Lafortune
    • 2
  1. 1.Boston UniversityUSA
  2. 2.The University of MichiganUSA

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