Random Number Generation
Pages: 39, b&w
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.
Introduction | Random Generation Functions | Seeding and Localization |
Methods | Statistical Distributions | References