Abstract
Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.
Keywords
Markov chainExamples of Markov chainsMarkov processComputer scienceContext (archaeology)Variable-order Markov modelProbability theoryStochastic processTheoretical computer scienceStatistical physicsMathematical economicsMarkov modelMathematicsMachine learningStatisticsPhysics
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Publication Info
- Year
- 1997
- Type
- book
- Citations
- 1112
- Access
- Closed
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Cite This
James R. Norris
(1997).
Markov Chains.
Cambridge University Press eBooks
.
https://doi.org/10.1017/cbo9780511810633
Identifiers
- DOI
- 10.1017/cbo9780511810633