Generating Random Deviates

Part of the Statistics and Computing book series (SCO)


Statisticians rely on a combination of mathematical theory and statistical simulation to develop new methods. Because simulations are often conducted on a massive scale, it is crucial that they be efficiently executed. In the current chapter, we investigate techniques for producing random samples from univariate and multivariate distributions. These techniques stand behind every successful simulation and play a critical role in Monte Carlo integration. Exceptionally fast code for simulations almost always depends on using a lower-level computer language such as C or Fortran. This limitation forces the statistician to write custom software. Mastering techniques for generating random variables (or deviates in this context) is accordingly a useful survival skill.


Success Probability Normal Deviate Standard Normal Deviate Random Deviate Adaptive Rejection Sampling 
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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© Springer New York 2010

Authors and Affiliations

  1. 1.Departments of Biomathematics, Human Genetics, and Statistics David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA

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