Basic Principles: Rejection, Weighting, and Others

  • Jun S. Liu
Part of the Springer Series in Statistics book series (SSS)


To generate random variables that follow a general probability distribution function π, we need first to generate random variables uniformly distributed in [0,1]. These random variables are often called random numbers for simplicity. However, this “simple-sounding” task is not easily achievable on a computer. But even if it were possible, it might not be desirable to use authentic random numbers because of the need to debug computer programs. In debugging a program, we often have to repeat the same computation many times; this require us to reproduce the same sequence of random numbers repeatedly. What becomes an accepted alternative in the community of scientific computing is to generate pseudo-random numbers.


Mean Square Error Importance Sampling Monte Carlo Computation Importance Weight Effective Sample Size 
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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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Jun S. Liu
    • 1
  1. 1.Department of StatisticsHarvard UniversityCambridgeUSA

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