Basic Principles: Rejection, Weighting, and Others
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.
KeywordsMean Square Error Importance Sampling Monte Carlo Computation Importance Weight Effective Sample Size
Unable to display preview. Download preview PDF.