AbstractEstimating real-world parameter values by means of Monte-Carlo/stochastic simulation is usually accomplished by carrying out a number‘n’of computer runs, each using random numbers taken from a pseudo-random number generator. In order to improve the accuracy of the estimate (reduce the estimate's variance), the most common recourse is to increasen, as the estimate's variance is inversely proportional ton. Variance reduction techniques provide an alternative to increasingn. They use statistical approaches which obtain more information from the computer runs conducted, or control and direct the pseudo-random streams to optimize the information likely to be produced by a run.