Computationally Intensive Methods

  • Warren J. Ewens
  • Gregory R. Grant
Part of the Statistics for Biology and Health book series (SBH)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Warren J. Ewens
    • 1
  • Gregory R. Grant
    • 2
  1. 1.Department of BiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Penn Center for Computational BiologyUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations