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Computationally Intensive Methods

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Part of the Statistics for Biology and Health book series (SBH)

Abstract

An important trend in statistical inference over the last twenty years has been the introduction of computationally intensive methods. These have been made possible by the availability of convenient and greatly increased computing power, and these methods are useful in bioinformatics and computational biology. Aspects of some computationally intensive methods used for both estimation and hypothesis testing are outlined in this chapter. Computationally intensive methods arise in both classical and Bayesian inference: We concentrate here on computationally intensive methods in classical inference.

Keywords

  • Null Hypothesis
  • Bootstrap Sample
  • Bootstrap Procedure
  • Bootstrap Estimate
  • Permutation Procedure

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_12
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© 2001 Springer Science+Business Media New York

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Ewens, W.J., Grant, G.R. (2001). Computationally Intensive Methods. In: Statistical Methods in Bioinformatics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3247-4_12

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  • DOI: https://doi.org/10.1007/978-1-4757-3247-4_12

  • 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