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Statistical Methods in Bioinformatics pp 129–145Cite as

Stochastic Processes (i): Poisson Processes and Markov Chains

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Abstract

In this section we state the fundamental properties that define a Poisson process, and from these properties we derive the Poisson distribution, introduced in Section 1.3.6.

Keywords

  • Markov Chain
  • Stationary Distribution
  • Poisson Process
  • Transition Matrix
  • Homogeneous Poisson 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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  • DOI: 10.1007/978-1-4757-3247-4_4
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Authors and Affiliations

  1. Department of Biology, University of Pennsylvania, 19104-6018, Philadelphia, PA, USA

    Warren J. Ewens

  2. Penn Center for Computational Biology, University of Pennsylvania, 19104, Philadelphia, PA, USA

    Gregory R. Grant

Authors
  1. Warren J. Ewens
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  2. Gregory R. Grant
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© 2001 Springer Science+Business Media New York

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Ewens, W.J., Grant, G.R. (2001). Stochastic Processes (i): Poisson Processes and Markov Chains. In: Statistical Methods in Bioinformatics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3247-4_4

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  • DOI: https://doi.org/10.1007/978-1-4757-3247-4_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4757-3249-8

  • Online ISBN: 978-1-4757-3247-4

  • eBook Packages: Springer Book Archive