Results

**1 - 3**of**3**### Pitfalls in Computation: Random and not so Random Numbers

, 2006

"... In simulation we often want computers to generate "random " numbers that are independent and have a known distribution. This is not so easy as it seems. We give some examples where the "random " numbers turned out to be not so random as expected, with potentially disastrous resul ..."

Abstract
- Add to MetaCart

In simulation we often want computers to generate "random " numbers that are independent and have a known distribution. This is not so easy as it seems. We give some examples where the "random " numbers turned out to be not so random as expected, with potentially disastrous results. To summarise, it is easy to produce random numbers when you don't want to, but not so easy when you do.

### High Dimensional Approximation

, 2007

"... A pseudo-random number generator (RNG) might be used to generate w-bit random samples in d dimensions if the number of state bits is at least dw. Some RNGs perform better than others and the concept of equidistribution has been introduced in the literature in order to rank different RNGs. In this ta ..."

Abstract
- Add to MetaCart

A pseudo-random number generator (RNG) might be used to generate w-bit random samples in d dimensions if the number of state bits is at least dw. Some RNGs perform better than others and the concept of equidistribution has been introduced in the literature in order to rank different RNGs. In this talk I shall define what it means for a RNG to be (d, w)-equidistributed, and then argue that (d, w)-equidistribution is not necessarily a desirable property. Presented at a Workshop on

### Random Numbers for Parallel Computers: Requirements and Methods

"... We examine the requirements and the available methods and software to provide (or imitate) uniform random numbers in parallel computing environments. In some settings, the goal is to use parallel processors to fill up rapidly a large array of random numbers. In other settings, thousands or millions ..."

Abstract
- Add to MetaCart

We examine the requirements and the available methods and software to provide (or imitate) uniform random numbers in parallel computing environments. In some settings, the goal is to use parallel processors to fill up rapidly a large array of random numbers. In other settings, thousands or millions of independent streams of random numbers are required, each one computed on a single processing element.