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Markov Chains

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

Abstract

A sequence of random variables \( X_0,X_1,... \) with values in a countable set S is a Markov chain if at any time n, the future states (or values) \( X_{n+1}, X_{n+2},... \) depend on the history \( X_0,...,X_n \) only through the present state \( X_n \).

Keywords

Markov Chain Random Walk Stationary Distribution Invariant Measure Transition Matrix 
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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