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Evolutionary Models

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Abstract

The contemporary biological data from which so many inferences are made are the result of evolution, that is, of an indescribably complicated stochastic process. Very simplified models of this process are often used in the literature, in particular for the construction of phylogenetic trees, and aspects of these simplified models are discussed in this chapter. The emphasis is on introductory statistical and probabilistic aspects. A probabilistic approach has the merit of allowing the testing of various hypotheses concerning the evolutionary process. Hypothesis-testing questions in the evolutionary context are discussed in Section 14.9.

Keywords

  • Markov Chain
  • Stationary Distribution
  • Transition Matrix
  • Maximum Likelihood Estimator
  • Spectral Expansion

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_13
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© 2001 Springer Science+Business Media New York

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Ewens, W.J., Grant, G.R. (2001). Evolutionary Models. In: Statistical Methods in Bioinformatics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3247-4_13

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  • DOI: https://doi.org/10.1007/978-1-4757-3247-4_13

  • 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