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Random Number Generation
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Random Number Generation
Pages: 39, b&w
ISBN: 978-1-57955-062-2
Year: 2008
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Description

The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, and numerically testing symbolic results. Such applications may require uniformly distributed numbers, nonuniformly distributed numbers, elements sampled with replacement, or elements sampled without replacement.

This tutorial covers the family of functions including RandomReal, RandomInteger, and RandomComplex, which generate uniformly distributed random numbers. RandomReal and RandomInteger also generate numbers for built-in distributions. RandomPrime generates primes within a range. The functions RandomChoice and RandomSample sample from a list of values with or without replacement. The elements may have equal or unequal weights. A framework is also included for defining additional methods and distributions for random number generation.

Table of Contents

Introduction | Random Generation Functions | Seeding and Localization | Methods | Statistical Distributions | References