Continuous-Time Markov Models

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

Abstract

In the previous chapter we studied stochastic models of randomly evolving systems that are observed at discrete-times n = 0, 1, 2,..., etc., with X n being the state of the system at time n. In this chapter we shall consider randomly evolving systems that are observed continuously at all times t ≥ 0, with X(t) being the state of the system at time t. The set of states that the system can be in at any given time is called the state space of the system and is denoted by S.

Keywords

Sojourn Time Rate Matrix Computational Problem Transition Probability Matrix Repair Time 
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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