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Generalized Markov Models

  • V.G. Kulkarni
Chapter
Part of the Springer Text in Statistics book series (STS)

Abstract

The main focus of this book is to study systems that evolve randomly in time. We encountered several applications in Chapter 5 where the system is observed at time n = 0, 1, 2, 3,.... In such a case we define X n as the state of the system at time n, and study the discrete-time stochastic process {X n , n ≥ 0}. In Chapter 5 we studied the systems that have the Markov property at each time n = 0, 1, 2, 3,..., i.e., the future of the system from any time n onward depends on its history up to time n only through the state of the system at time n.

Keywords

Renewal Process Sojourn Time Transition Probability Matrix Repair Time Cumulative 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 Science+Business Media New York 1999

Authors and Affiliations

  • V.G. Kulkarni
    • 1
  1. 1.Department of Operations ResearchUniversity of North CarolinaChapel HillUSA

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