Chapter

Handbook of Computational Statistics

Part of the series Springer Handbooks of Computational Statistics pp 35-71

Date:

Random Number Generation

  • Pierre L’EcuyerAffiliated withDépartement d’Informatique et de Recherche Opérationnelle, Université de Montréal Email author 

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Abstract

The fields of probability and statistics are built over the abstract concepts of probability space and random variable. This has given rise to elegant and powerful mathematical theory, but exact implementation of these concepts on conventional computers seems impossible. In practice, random variables and other random objects are simulated by deterministic algorithms. The purpose of these algorithms is to produce sequences of numbers or objects whose behavior is very hard to distinguish from that of their “truly random” counterparts, at least for the application of interest. Key requirements may differ depending on the context.For Monte Carlo methods, the main goal is to reproduce the statistical properties on which these methods are based, so that the Monte Carlo estimators behave as expected, whereas for gambling machines and cryptology, observing the sequence of output values for some time should provide no practical advantage for predicting the forthcoming numbers better than by just guessing at random.