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Generalized Markov Models

  • V. G. Kulkarni
Chapter
Part of the Springer Texts 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 2 where the system is observed at time n = 0, 1, 2, 3,.... In such cases, 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 2, 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 nonward depends on its history up to time nonly through the state of the system at time n. We found this property to be immensely helpful in studying the behavior of these systems.

Keywords

Renewal Process Sojourn Time Transition Probability Matrix Repair Time Emergency Patient 
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, LLC 2011

Authors and Affiliations

  1. 1.Department of Statistics and Operations ResearchUniversity of North CarolinaChapel HillUSA

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